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U.S.S.R. Computational Mathematics and Mathematical Physics 1986: Volume 26 , Issue Contents. Digitized from IA1646504-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1986_26_index_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1986_26_1_0 .

Computational Mathematics and Mathematical Physics 1992: Volume 32 , Issue Contents. Digitized from IA1646504-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1991_31_12_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1992_32_1_0 .

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.

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.

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

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.

U.S.S.R. Computational Mathematics and Mathematical Physics 1987: Volume 27 , Issue Index. Digitized from IA1652722-06 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1987_27_contents . Next issue: sim_computational-mathematics-and-mathematical-physics_1987_27_1 .

U.S.S.R. Computational Mathematics and Mathematical Physics 1988: Volume 28 , Issue Index. Digitized from IA1646504-04 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1988_28_contents_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1988_28_1_0 .

Computational Mathematics and Mathematical Physics 1994: Volume 34 , Issue CONTENTS. Digitized from IA1646504-02 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1993_33_12_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1994_34_1_0 .

Computational Mathematics and Mathematical Physics 1994: Volume 34 , Issue CONTENTS. Digitized from IA1646504-02 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1993_33_12_0 . Next issue: sim_computational-mathematics-and-mathematical-physics_1994_34_1_0 .

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.

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

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.

In a first course to classical mechanics elementary physical processes like elastic two-body collisions, the mass-spring model, or the gravitational two-body problem are discussed in detail. The continuation to many-body systems, however, is defered to graduate courses although the underlying equations of motion are essentially the same and although there is a strong motivation for high- school students in particular because of the use of particle systems in computer games. The missing link between the simple and the more complex problem is a basic introduction to solve the equations of motion numerically which could be illustrated, however, by means of the Euler method. The many-particle physics simulation package MPPhys offers a platform to experiment with simple particle simulations. The aim is to give a principle idea how to implement many-particle simulations and how simulation and visualization can be combined for interactive visual explorations.

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.

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.

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.

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 .

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 .

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 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 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

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 .

According to the Church-Turing Thesis (CTT), effective formal behaviours can be simulated by Turing machines; this has naturally led to speculation that physical systems can also be simulated computationally. But is this wider claim true, or do behaviours exist which are strictly hypercomputational? Several idealised computational models are known which suggest the possibility of hypercomputation, some Newtonian, some based on cosmology, some on quantum theory. While these models' physicality is debatable, they nonetheless throw into question the validity of extending CTT to include all physical systems. We consider the physicality of hypercomputational behaviour from first principles, by showing that quantum theory can be reformulated in a way that explains why physical behaviours can be regarded as 'computing something' in the standard computational state-machine sense. While this does not rule out the physicality of hypercomputation, it strongly limits the forms it can take. Our model also has physical consequences; in particular, the continuity of motion and arrow of time become theorems within the basic model.

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.

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 .

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 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.

v. : 23 cm

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

computational physics book

computational physics book

v. : 24 cm

v. : 24 cm

v. : 24 cm

v. : 24 cm

v. : 24 cm

v. : 24 cm

We consider optimization problems that arise when estimating a set of unknown parameters from experimental data, particularly in the context of nuclear density functional theory. We examine the cost of not having derivatives of these functionals with respect to the parameters. We show that the POUNDERS code for local derivative-free optimization obtains consistent solutions on a variety of computationally expensive energy density functional calibration problems. We also provide a primer on the operation of the POUNDERS software in the Toolkit for Advanced Optimization.

U.S.S.R. Computational Mathematics and Mathematical Physics 1980: Volume 20 , Issue INDEX/CONTENTS. Digitized from IA1646333-05 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1979_19_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1980_20_1 .

U.S.S.R. Computational Mathematics and Mathematical Physics 1980: Volume 20 , Issue INDEX/CONTENTS. Digitized from IA1646333-05 . Previous issue: sim_computational-mathematics-and-mathematical-physics_1979_19_6 . Next issue: sim_computational-mathematics-and-mathematical-physics_1980_20_1 .

v. : 24 cm