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Renormalization group procedure for effective particles (RGPEP) is applied in terms of a second-order perturbative computation to an Abelian gauge theory, as an example of application worth studying on the way toward derivation of a…

High Energy Physics - Theory · Physics 2020-02-12 Stanislaw D. Glazek

The objective of offline RL is to learn optimal policies when a fixed exploratory demonstrations data-set is available and sampling additional observations is impossible (typically if this operation is either costly or rises ethical…

Machine Learning · Computer Science 2021-06-10 Firas Jarboui , Vianney Perchet

We present a new multi-dimensional, robust, and cell-centered finite-volume scheme for the ideal MHD equations. This scheme relies on relaxation and splitting techniques and can be easily used at high order. A fully conservative version is…

The "backward simulation" of a stochastic process is defined as the stochastic dynamics that trace a time-reversed path from the target region to the initial configuration. If the probabilities calculated by the original simulation are…

Data Analysis, Statistics and Probability · Physics 2019-01-29 Shinichi Takayanagi , Yukito Iba

Traditional control theory-based methods require tailored engineering for each system and constant fine-tuning. In power plant control, one often needs to obtain a precise representation of the system dynamics and carefully design the…

Systems and Control · Electrical Eng. & Systems 2024-09-21 Yixuan Sun , Sami Khairy , Richard B. Vilim , Rui Hu , Akshay J. Dave

Gyrokinetic modeling is appropriate for describing Tokamak plasma turbulence, and the gyroaverage operator is a cornerstone of this approach. In a gyrokinetic code, the gyroaveraging scheme needs to be accurate enough to avoid spoiling the…

Computational Physics · Physics 2016-02-10 F Rozar , C Steiner , G Latu , M Mehrenberger , V Grandgirard , Julien Bigot , T Cartier-Michaud , Jean Roman

The particle-in-cell approach has proven effective at modeling neutron star and black hole magnetospheres from first principles, but global simulations are plagued with an unrealistically small separation between the scales where…

High Energy Astrophysical Phenomena · Physics 2024-10-09 Adrien Soudais , Benoît Cerutti , Ioannis Contopoulos

We study the convergence and the stability of fictitious dynamical methods for electrons. First, we show that a particular damped second-order dynamics has a much faster rate of convergence to the ground-state than first-order steepest…

Condensed Matter · Physics 2009-10-22 Francesco Tassone , Francesco Mauri , Roberto Car

A particle-in-cell algorithm is derived with a canonical Poisson structure in the formalism of finite element exterior calculus. The resulting method belongs to the class of gauge-compatible splitting algorithms, which exactly preserve…

Plasma Physics · Physics 2022-05-05 Alexander S. Glasser , Hong Qin

A scheme is proposed for the subtraction of soft and collinear divergences present in massless real emission phase space integrals. The scheme is based on a local slicing procedure which utilises the soft and collinear factorisation…

High Energy Physics - Phenomenology · Physics 2018-08-29 Franz Herzog

A dynamic backstepping method is proposed to design controllers for nonlinear systems in the pure-feedback form, for which the traditional backstepping method suffers from solving the implicit nonlinear algebraic equation. The idea of this…

Systems and Control · Computer Science 2017-06-28 Sheng Zhang , Wei-qi Qian

This paper is aimed at improving the performance of the treecode algorithm for N-Body simulation by employing the NetSolve GridRPC programming model to exploit the use of multiple clusters. N-Body is a classical problem, and appears in many…

Distributed, Parallel, and Cluster Computing · Computer Science 2012-11-13 Truong Vinh Truong Duy , Katsuhiro Yamazaki , Shigeru Oyanagi

High order algorithms have emerged in numerical astrophysics as a promising avenue to reduce truncation error (proportional to a power of the linear resolution $\Delta x$) with only a moderate increase to computational expense. Significant…

Instrumentation and Methods for Astrophysics · Physics 2025-02-27 Tomoyuki Hanawa , Patrick D. Mullen

Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive…

Machine Learning · Statistics 2014-10-27 Vincent Dumoulin , Ian J. Goodfellow , Aaron Courville , Yoshua Bengio

This comment clarifies the relation of the research in a recently published article [Phys. Plasmas 14, 042503 (2007)] to other prior publications addressing the inclusion of electromagnetic and drift-kinetic electron physics in gyrokinetic…

Plasma Physics · Physics 2011-11-10 Bruce I. Cohen , Andris M. Dimits , William M. Nevins

In many contexts the modal properties of a structure change, either due to the impact of a changing environment, fatigue, or due to the presence of structural damage. For example during flight, an aircraft's modal properties are known to…

Machine Learning · Computer Science 2018-12-12 Prasad Cheema , Mehrisadat M. Alamdari , Gareth A. Vio

Evaluating multi-center molecular integrals with Cartesian Gaussian-type basis sets has been a long-standing bottleneck in electronic structure theory calculation for solids and molecules. We have developed a vector-coupling and…

Quantum Physics · Physics 2024-05-17 Hang Hu , Gilles Peslherbe , Hsu Kiang Ooi , Anguang Hu

As a very well-known classical fact, non-minimum phase zeros of the process put some limitations on the performance of the feedback system. The source of these limitations is that such non-minimum phase zeros cannot be cancelled by unstable…

Optimization and Control · Mathematics 2012-07-31 Farshad Merrikh-Bayat

In this paper, we compare the performance of different numerical schemes in approximating Pontryagin's Maximum Principle's necessary conditions for the optimal control of nonholonomic systems. Retraction maps are used as a seed to construct…

In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent…

Machine Learning · Computer Science 2023-01-04 Franck Djeumou , Christian Ellis , Murat Cubuktepe , Craig Lennon , Ufuk Topcu
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