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Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation $\DelH$ and standard Gibbs energy of formation $\DelG$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated $\DelH$ ($\DelG$) for the 180 molecules is reduced from 21.4 (22.3) kcal$\cdotp$mol$^{-1}$ to 3.1 (3.3) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G({\it d,p}) and from 12.0 (12.9) kcal$\cdotp$mol$^{-1}$ to 3.3 (3.4) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G(3{\it df},2{\it p}) before and after the neural-network correction.

Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation $\DelH$ and standard Gibbs energy of formation $\DelG$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated $\DelH$ ($\DelG$) for the 180 molecules is reduced from 21.4 (22.3) kcal$\cdotp$mol$^{-1}$ to 3.1 (3.3) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G({\it d,p}) and from 12.0 (12.9) kcal$\cdotp$mol$^{-1}$ to 3.3 (3.4) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G(3{\it df},2{\it p}) before and after the neural-network correction.

Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation $\DelH$ and standard Gibbs energy of formation $\DelG$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated $\DelH$ ($\DelG$) for the 180 molecules is reduced from 21.4 (22.3) kcal$\cdotp$mol$^{-1}$ to 3.1 (3.3) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G({\it d,p}) and from 12.0 (12.9) kcal$\cdotp$mol$^{-1}$ to 3.3 (3.4) kcal$\cdotp$mol$^{-1}$ for B3LYP/6-311+G(3{\it df},2{\it p}) before and after the neural-network correction.

An overview of Northrop programs in computational physics is presented. These programs depend on access to today's supercomputers, such as the Numerical Aerodynamical Simulator (NAS), and future growth on the continuing evolution of computational engines. Descriptions here are concentrated on the following areas: computational fluid dynamics (CFD), computational electromagnetics (CEM), computer architectures, and expert systems. Current efforts and future directions in these areas are presented. The impact of advances in the CFD area is described, and parallels are drawn to analagous developments in CEM. The relationship between advances in these areas and the development of advances (parallel) architectures and expert systems is also presented.

U.S.S.R. Computational Mathematics and Mathematical Physics 1994: Volume 34 , Issue INDEX/CONTENTS. Digitized from IA1652722-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1993_33_12 . Next issue: sim_computational-mathematics-and-mathematical-physics_1994_34_1 .

U.S.S.R. Computational Mathematics and Mathematical Physics 1994: Volume 34 , Issue INDEX/CONTENTS. Digitized from IA1652722-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1993_33_12 . Next issue: sim_computational-mathematics-and-mathematical-physics_1994_34_1 .

This public document was automatically mirrored from PDFy . Original filename: Foundations of Physics Volume 42 issue 3 2012 [doi 10.1007%2Fs10701-011-9601-0] Todd A. Brun; Mark M. Wilde -- Perfect State Distinguishability and Computational Speedups with Postselected Closed Timelike Curves.pdf URL: https://pdf.yt/d/h1JSrxleqUv_yL5U Upload date: September 03, 2014 05:23:12

This paper presents viewgraphs on computational physics for space flight applications. The topics include: 1) Introduction to space radiation effects in microelectronics; 2) Using applied physics to help NASA meet mission objectives; 3) Example of applied computational physics; and 4) Future directions in applied computational physics.

This is a brief review paper summarizing talks at the NATO school on Complexity and Large Deviations in Geilo, Norway, 2001.

In this paper, we review some features of quantum annealing and related topics from viewpoints of statistical physics, condensed matter physics, and computational physics. We can obtain a better solution of optimization problems in many cases by using the quantum annealing. Actually the efficiency of the quantum annealing has been demonstrated for problems based on statistical physics. Then the quantum annealing has been expected to be an efficient and generic solver of optimization problems. Since many implementation methods of the quantum annealing have been developed and will be proposed in the future, theoretical frameworks of wide area of science and experimental technologies will be evolved through studies of the quantum annealing.

To increase public awareness of theoretical materials physics, a small group of high school students is invited to participate actively in a current research projects at Chalmers University of Technology. The Chalmers research group explores methods for filtrating hazardous and otherwise unwanted molecules from drinking water, for example by adsorption in active carbon filters. In this project, the students use graphene as an idealized model for active carbon, and estimate the energy of adsorption of the methylbenzene toluene on graphene with the help of the atomic-scale calculational method density functional theory. In this process the students develop an insight into applied quantum physics, a topic usually not taught at this educational level, and gain some experience with a couple of state-of-the-art calculational tools in materials research.

To increase public awareness of theoretical materials physics, a small group of high school students is invited to participate actively in a current research projects at Chalmers University of Technology. The Chalmers research group explores methods for filtrating hazardous and otherwise unwanted molecules from drinking water, for example by adsorption in active carbon filters. In this project, the students use graphene as an idealized model for active carbon, and estimate the energy of adsorption of the methylbenzene toluene on graphene with the help of the atomic-scale calculational method density functional theory. In this process the students develop an insight into applied quantum physics, a topic usually not taught at this educational level, and gain some experience with a couple of state-of-the-art calculational tools in materials research.

The paper describes two general problems encountered in computational assignments at the introductory level. First, novice students often treat computer code as almost magic incantations, and like novices in many fields, have trouble creating new algorithms or procedures to solve novel problems. Second, the nature of computational studies often means that the results generated are interpreted via theoretically devised quantities, which may not meet a student's internal standards for proof when compared to an experimental measurement. The paper then offers a lab/programming assignment, used in a calculus-based physics course, which was devised to address these problems. In the assignment, students created a computational model of the heat flow involved in heating an iron rod in a blacksmith's forge. After creating the simulation, students attended a blacksmithing seminar and had a chance to work with iron and take data on its heating in a coke-fueled forge. On their return to campus, students revised their computational models in light of their experimental data.

The paper describes two general problems encountered in computational assignments at the introductory level. First, novice students often treat computer code as almost magic incantations, and like novices in many fields, have trouble creating new algorithms or procedures to solve novel problems. Second, the nature of computational studies often means that the results generated are interpreted via theoretically devised quantities, which may not meet a student's internal standards for proof when compared to an experimental measurement. The paper then offers a lab/programming assignment, used in a calculus-based physics course, which was devised to address these problems. In the assignment, students created a computational model of the heat flow involved in heating an iron rod in a blacksmith's forge. After creating the simulation, students attended a blacksmithing seminar and had a chance to work with iron and take data on its heating in a coke-fueled forge. On their return to campus, students revised their computational models in light of their experimental data.

The paper describes two general problems encountered in computational assignments at the introductory level. First, novice students often treat computer code as almost magic incantations, and like novices in many fields, have trouble creating new algorithms or procedures to solve novel problems. Second, the nature of computational studies often means that the results generated are interpreted via theoretically devised quantities, which may not meet a student's internal standards for proof when compared to an experimental measurement. The paper then offers a lab/programming assignment, used in a calculus-based physics course, which was devised to address these problems. In the assignment, students created a computational model of the heat flow involved in heating an iron rod in a blacksmith's forge. After creating the simulation, students attended a blacksmithing seminar and had a chance to work with iron and take data on its heating in a coke-fueled forge. On their return to campus, students revised their computational models in light of their experimental data.

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue CONTENTS. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_index .

Under sponsorship from various Department of Defense (DoD) organizations and a cooperative Research and Development Agreement (CRADA) with the U.S. Research Development and Engineering Command, Army Research Laboratory (RDECOM-ARL) HyPerComp has significantly advanced the state of the art of time-domain, and broad band electromagnetic simulations. The TEMPUS (Time-Domain ElectroMagnetic Parallel Unstructured Simulator) environment is a complete self contained code suite that includes computer aided design (CAD) geometry creation and repair unstructured gridding for fell-scale targets with general materials scalable parallel code architecture higher-order accurate discontinuous Galerkin solvers for Maxwell's equations and post processing utilities for solution visualization and exaction of final results like bistatic and monostatic radar cross section (RCS) synthetic aperture radar (SAR) images and high-range-resolution (HRR) profiles. The high-performance TEMPUS environment is well suited for modeling a variety of targets and electromagnetic problems of interest to the U.S. Army such as: 1) high- speed projectiles with subtle surface discontinuities ridges and/or fins 2) ground-based targets such as tanks and scud missile launchers and 3) foliage penetration and ground interaction for target under trees (TUT). Some of the physics-based phenomenological features that govern the electromagnetic response of general targets are: a) specular reflection b) creeping waves c) traveling waves) slow moving surface waves e) edge diffraction f) singular currents at surface discontinuities g) resonating gaps and cavities and h) general material response.

Under sponsorship from various Department of Defense (DoD) organizations and a cooperative Research and Development Agreement (CRADA) with the U.S. Research Development and Engineering Command, Army Research Laboratory (RDECOM-ARL) HyPerComp has significantly advanced the state of the art of time-domain, and broad band electromagnetic simulations. The TEMPUS (Time-Domain ElectroMagnetic Parallel Unstructured Simulator) environment is a complete self contained code suite that includes computer aided design (CAD) geometry creation and repair unstructured gridding for fell-scale targets with general materials scalable parallel code architecture higher-order accurate discontinuous Galerkin solvers for Maxwell's equations and post processing utilities for solution visualization and exaction of final results like bistatic and monostatic radar cross section (RCS) synthetic aperture radar (SAR) images and high-range-resolution (HRR) profiles. The high-performance TEMPUS environment is well suited for modeling a variety of targets and electromagnetic problems of interest to the U.S. Army such as: 1) high- speed projectiles with subtle surface discontinuities ridges and/or fins 2) ground-based targets such as tanks and scud missile launchers and 3) foliage penetration and ground interaction for target under trees (TUT). Some of the physics-based phenomenological features that govern the electromagnetic response of general targets are: a) specular reflection b) creeping waves c) traveling waves) slow moving surface waves e) edge diffraction f) singular currents at surface discontinuities g) resonating gaps and cavities and h) general material response.

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An advanced program in Computational Physics can conduct research III students in broad areas of physics and computational technology, and be responsive to needs of the Department of Defense. The first serves the Departmental mission, the second gives the preparation for careers in physics or in informational technology (IT), and the third advances our military preparedness. We note that the IT sector currently has approximately one million unfilled opportunities in the United States. Three areas of computationally based research have been selected with these criteria. They are: (1) power compression technology, specifically the study of ultrahigh (approximately equal 10(exp 21) W/cu cm) power densities, (2) basic physical interactions, which has potential applications to navigation and code-breaking, and (3) protein dynamics, an area related to new potentialities in defense against biological attack. The computational equipment associated with this program have yielded results on: (A) the stability of self-trapped plasma channels produced with intense femtosecond ultraviolet (248 mm) pulses, (B) the mechanism of gamma-ray bursts, (C) the fusion of the concepts of mass and space, and (D) the potential existence of a new organizing-principle in biology that has a direct physical counterpart in the group structure of elementary particle states.

This DURIP equipment award was used to purchase, install, and bring on-line a computational cluster for plasma physics simulations. An SGI integrated cluster environment ICE unit was purchased, featuring 192 2.8 GHz processors (on 24 nodes) with 384 GB highspeed RAM on an Infiniband backbone. Additional nodes include an 8-processor login node (32 GB RAM) and a data server with a 6 TB RAID6 Infiniband system, plus admin and rack leader nodes. SGI personnel setup the system on-site, with additional integration performed by our local IT personnel. SGI also installed cluster control software and licenses for the SUSE-based Linux operating system. Software was purchased and installed including Intel Fortran and C/C++ compiler suites (debuggers, performance measurements, etc.), the TotalView parallel debugger, and the MOAB/PBS scheduling software. Comparisons were made amongst several cluster manufacturers, including Cray, IBM, Dell, Silicon Mechanics, Rackable Sytems, and SiCortex before deciding on the SGI ICE model as the most turn-key system. The system was ordered in late 2008 and arrived in early 2009 and was up and running routinely with local users in a few weeks. Local University of Washington computational groups have made extensive use of the DURIP cluster, running several different advanced extended MHD/two-fluid codes. This DURIP award has greatly increased turnaround time and productivity for these groups.

This is a Wolfram Physics Project working session about the axiomatization of the Computational Universe. Begins at 1:36 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announcement post: http://wolfr.am/physics-announcement Find the tools to build a universe: https://wolfr.am/physics-tools Find the technical documents: https://wolfr.am/physics-documents Follow us on our official social media channels. Twitter: https://twitter.com/WolframResearch/ Facebook: https://www.facebook.com/wolframresearch/ Instagram: https://www.instagram.com/wolframresearch/ LinkedIn: https://www.linkedin.com/company/wolfram-research/ Stephen Wolfram's Twitter: https://twitter.com/stephen_wolfram/ Contribute to the official Wolfram Community: https://community.wolfram.com/ Stay up-to-date on the latest interest at Wolfram Research through our blog: https://blog.wolfram.com/ Follow Stephen Wolfram's life, interests, and what makes him tick on his blog: https://writings.stephenwolfram.com/

This is a Wolfram Physics Project working session about the axiomatization of the Computational Universe. Begins at 1:36 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announcement post: http://wolfr.am/physics-announcement Find the tools to build a universe: https://wolfr.am/physics-tools Find the technical documents: https://wolfr.am/physics-documents Follow us on our official social media channels. Twitter: https://twitter.com/WolframResearch/ Facebook: https://www.facebook.com/wolframresearch/ Instagram: https://www.instagram.com/wolframresearch/ LinkedIn: https://www.linkedin.com/company/wolfram-research/ Stephen Wolfram's Twitter: https://twitter.com/stephen_wolfram/ Contribute to the official Wolfram Community: https://community.wolfram.com/ Stay up-to-date on the latest interest at Wolfram Research through our blog: https://blog.wolfram.com/ Follow Stephen Wolfram's life, interests, and what makes him tick on his blog: https://writings.stephenwolfram.com/

This is a Wolfram Physics Project working session about the axiomatization of the Computational Universe. Begins at 1:36 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announcement post: http://wolfr.am/physics-announcement Find the tools to build a universe: https://wolfr.am/physics-tools Find the technical documents: https://wolfr.am/physics-documents Follow us on our official social media channels. Twitter: https://twitter.com/WolframResearch/ Facebook: https://www.facebook.com/wolframresearch/ Instagram: https://www.instagram.com/wolframresearch/ LinkedIn: https://www.linkedin.com/company/wolfram-research/ Stephen Wolfram's Twitter: https://twitter.com/stephen_wolfram/ Contribute to the official Wolfram Community: https://community.wolfram.com/ Stay up-to-date on the latest interest at Wolfram Research through our blog: https://blog.wolfram.com/ Follow Stephen Wolfram's life, interests, and what makes him tick on his blog: https://writings.stephenwolfram.com/

This is a Wolfram Physics Project working session about the axiomatization of the Computational Universe. Begins at 1:36 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announcement post: http://wolfr.am/physics-announcement Find the tools to build a universe: https://wolfr.am/physics-tools Find the technical documents: https://wolfr.am/physics-documents Follow us on our official social media channels. Twitter: https://twitter.com/WolframResearch/ Facebook: https://www.facebook.com/wolframresearch/ Instagram: https://www.instagram.com/wolframresearch/ LinkedIn: https://www.linkedin.com/company/wolfram-research/ Stephen Wolfram's Twitter: https://twitter.com/stephen_wolfram/ Contribute to the official Wolfram Community: https://community.wolfram.com/ Stay up-to-date on the latest interest at Wolfram Research through our blog: https://blog.wolfram.com/ Follow Stephen Wolfram's life, interests, and what makes him tick on his blog: https://writings.stephenwolfram.com/

This is a Wolfram Physics Project working session about the axiomatization of the Computational Universe. Begins at 1:36 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announcement post: http://wolfr.am/physics-announcement Find the tools to build a universe: https://wolfr.am/physics-tools Find the technical documents: https://wolfr.am/physics-documents Follow us on our official social media channels. Twitter: https://twitter.com/WolframResearch/ Facebook: https://www.facebook.com/wolframresearch/ Instagram: https://www.instagram.com/wolframresearch/ LinkedIn: https://www.linkedin.com/company/wolfram-research/ Stephen Wolfram's Twitter: https://twitter.com/stephen_wolfram/ Contribute to the official Wolfram Community: https://community.wolfram.com/ Stay up-to-date on the latest interest at Wolfram Research through our blog: https://blog.wolfram.com/ Follow Stephen Wolfram's life, interests, and what makes him tick on his blog: https://writings.stephenwolfram.com/

Efficient modeling and computation of the nonlinear interaction of fluid with a solid undergoing nonlinear deformation has remained a challenging problem in computational science and engineering. Direct numerical simulation of the non-linear equations, governing even the most simplified fluid-structure interaction model depends on the convergence of iterative solvers which in turn relies heavily on the properties of the coupled system. The purpose of this work is to model and simulate multi-physics applications that involve fluid-structure interaction using a distributed multilevel algorithm with finite elements. The proposed algorithm is tested using COMSOL which offers the flexibility and efficiency to study coupled problems involving fluid-structure interaction. Numerical results for some benchmark fluid-structure interactions are presented that validate the proposed computational methodology for solving coupled problems involving fluid-structure interaction is reliable and robust.

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

Personalization is the process of fitting a model to patient data, a critical step towards application of multi-physics computational models in clinical practice. Designing robust personalization algorithms is often a tedious, time-consuming, model- and data-specific process. We propose to use artificial intelligence concepts to learn this task, inspired by how human experts manually perform it. The problem is reformulated in terms of reinforcement learning. In an off-line phase, Vito, our self-taught artificial agent, learns a representative decision process model through exploration of the computational model: it learns how the model behaves under change of parameters. The agent then automatically learns an optimal strategy for on-line personalization. The algorithm is model-independent; applying it to a new model requires only adjusting few hyper-parameters of the agent and defining the observations to match. The full knowledge of the model itself is not required. Vito was tested in a synthetic scenario, showing that it could learn how to optimize cost functions generically. Then Vito was applied to the inverse problem of cardiac electrophysiology and the personalization of a whole-body circulation model. The obtained results suggested that Vito could achieve equivalent, if not better goodness of fit than standard methods, while being more robust (up to 11% higher success rates) and with faster (up to seven times) convergence rate. Our artificial intelligence approach could thus make personalization algorithms generalizable and self-adaptable to any patient and any model.

This book encapsulates the coverage for a two-semester course in computational physics. The first part introduces the basic numerical methods while omitting mathematical proofs but demonstrating the algorithms by way of numerous computer experiments. The second part specializes in simulation of classical and quantum systems with instructive examples spanning many fields in physics, from a classical rotor to a quantum bit. All program examples are realized as Java applets ready to run in your browser and do not require any programming skills

A brief comment on A variational Bayesian approach for inverse problems with skew-t error distributions (Guha et al., Journal of Computational Physics 301 (2015) 377-393) is given in this letter.

Computational Mathematics and Mathematical Physics 1995: Volume 35 , Issue CONTENTS. Digitized from IA1646504-02 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1994_34_12_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1995_35_1_0 .

U.S.S.R. Computational Mathematics and Mathematical Physics 1981: Volume 21 , Issue INDEX/CONTENTS. Digitized from IA1646333-05 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1980_20_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1981_21_1 .

Computational Mathematics and Mathematical Physics 1993: Volume 33 , Issue CONTENTS. Digitized from IA1646504-02 . Next issue: sim_computational-mathematics-and-mathematical-physics_1993_33_1_0 .

The present investigation is a continuation of a previous joint project between the Florida State University and the NASA Langley Research Center Liner Physics Team. In the previous project, a study of acoustic liners, in two dimensions, inside a normal incidence impedance tube was carried out. The study consisted of two parts. The NASA team was responsible for the experimental part of the project. This involved performing measurements in an impedance tube with a large aspect ratio slit resonator. The FSU team was responsible for the computation part of the project. This involved performing direct numerical simulation (DNS) of the NASA experiment in two dimensions using CAA methodology. It was agreed that upon completion of numerical simulation, the computed values of the liner impedance were to be sent to NASA for validation with experimental results. On following this procedure good agreements were found between numerical results and experimental measurements over a wide range of frequencies and sound-pressure-level. Broadband incident sound waves were also simulated numerically and measured experimentally. Overall, good agreements were also found.

The authors of this paper have provided a set of ready-to-run FORTRAN programs that should be useful in the field of theoretical nuclear physics. The purpose of this document is to provide a simple synopsis of the programs and their use. A separate section is devoted to each program set and includes: abstract; files; compiling, linking, and running; obtaining results; and a tutorial.

Procedures and methods for veri.cation of coding algebra and for validations of models and calculations used in the aerospace computational fluid dynamics (CFD) community would be ef.cacious if used by the glacier dynamics modeling community. This paper presents some of those methods, and how they might be applied to uncertainty management supporting code veri.cation and model validation for glacier dynamics. The similarities and differences between their use in CFD analysis and the proposed application of these methods to glacier modeling are discussed. After establishing sources of uncertainty and methods for code veri.cation, the paper looks at a representative sampling of veri.cation and validation efforts that are underway in the glacier modeling community, and establishes a context for these within an overall solution quality assessment. Finally, a vision of a new information architecture and interactive scienti.c interface is introduced and advocated.

We assess the Tognetti-Cortona-Adamo (TCA) generalized gradient approximation correlation functional [J. Chem. Phys. 128:034101 (2008)] for a variety of electronic systems. We find that, even if the TCA functional is not exact for the uniform electron gas, it is very accurate for the jellium surface correlation energies and it gives a realistic description of the quantum oscillations and surface effects of various jellium clusters, that are important model systems in computational chemistry and solid-state physics. When the TCA correlation is combined with the non-empirical PBEint, Wu-Cohen, and PBEsol$_b$ exchange functionals, the resulting exchange-correlation approximations provide good performances for a broad palette of systems and properties, being reasonably accurate for thermochemistry and geometry of molecules, transition metal complexes, non-covalent interactions,equilibrium lattice constants, bulk moduli, and cohesive energies of solids.

We assess the Tognetti-Cortona-Adamo (TCA) generalized gradient approximation correlation functional [J. Chem. Phys. 128:034101 (2008)] for a variety of electronic systems. We find that, even if the TCA functional is not exact for the uniform electron gas, it is very accurate for the jellium surface correlation energies and it gives a realistic description of the quantum oscillations and surface effects of various jellium clusters, that are important model systems in computational chemistry and solid-state physics. When the TCA correlation is combined with the non-empirical PBEint, Wu-Cohen, and PBEsol$_b$ exchange functionals, the resulting exchange-correlation approximations provide good performances for a broad palette of systems and properties, being reasonably accurate for thermochemistry and geometry of molecules, transition metal complexes, non-covalent interactions,equilibrium lattice constants, bulk moduli, and cohesive energies of solids.

The author, as part of an internship under the Joint Educational Opportunities for Minorities (JEOM) Program at Naval Air Warfare Center Weapons Division (NAWCWD), China Lake, California, has devised models and methods for the solution of certain frequently recurring problems in electrical engineering. Starting with fundamental equations relating to line charges, the author utilizes the well-known method of moments to model single strip charges, multiple strip charges in various geometrical configurations, and a charged square cylinder excited by an incidental plane wave. Finally, the author considers the problem of calculating the current along a thin-wire antenna.

The author, as part of an internship under the Joint Educational Opportunities for Minorities (JEOM) Program at Naval Air Warfare Center Weapons Division (NAWCWD), China Lake, California, has devised models and methods for the solution of certain frequently recurring problems in electrical engineering. Starting with fundamental equations relating to line charges, the author utilizes the well-known method of moments to model single strip charges, multiple strip charges in various geometrical configurations, and a charged square cylinder excited by an incidental plane wave. Finally, the author considers the problem of calculating the current along a thin-wire antenna.

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v. : 23 cm

U.S.S.R. Computational Mathematics and Mathematical Physics 1979: Volume 19 , Issue INDEX/CONTENTS. Digitized from IA1646333-05 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1978_18_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1979_19_1 .

U.S.S.R. Computational Mathematics and Mathematical Physics 1988: Volume 28 , Issue Contents. Digitized from IA1646504-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1987_27_6_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1988_28_index_0 .

Contents: Introduction; The Classical Few-Body Problem; Partial Differential Equations;The classical N-body problem; Integration methods; Percolation; Magnetic systems; The quantum one-body problem; The quantum N body problem: quantum chemistry methods; The quantum N body problem: exact algorithms. Lecture Notes Collection FreeScience.info ID2503 Obtained from http://www.ifb.ethz.ch/education/IntroductionComPhys/ScriptTroyer.pdf http://www.freescience.info/go.php?pagename=books&id=2503

v. : 24 cm

v. : 24 cm

v. : 24 cm