Computational Physics - Science topic

The detailed behavior of neutrons in a rapidly changing time-dependent physical system is a challenging computational physics problem, particularly when using Monte Carlo methods on heterogeneous high-performance computing architectures. A small number of algorithms and code implementations have been shown to be performant for time-independent (fix...

In the past decades, multiple shooting methods have proven to be a promising direction to speed up the optimization process, especially in the context of ODE-based optimization. Very recently, Fang et al. (Journal of Computational Physics, vol. 452, 110926, 2022) proposed a multiple shooting algorithm for large-scale PDE-constrained optimization. T...

In recent years, Physics-Informed Neural Networks (PINNs) have drawn great interest among researchers as a tool to solve computational physics problems. Unlike conventional neural networks, which are black-box models that “blindly” establish a correlation between input and output variables using a large quantity of labeled data, PINNs directly embe...

We present here a computational numerical operator, and we name it as Time Invariance Operator (TIO). This operator can add obstacles to the domain of the differential equation that describes a physical phenomenon. After the TIO acts, the wave equation recognizes the introduced points as non-interacting zones without affecting the rest of the domai...

I present a novel equivariant neural network architecture for the large-scale spin dynamics simulation of the Kondo lattice model. This neural network mainly consists of tensor-product-based convolution layers and ensures two equivariances: translations of the lattice and rotations of the spins. I implement equivariant neural networks for two Kond...

The variational approach is a cornerstone of computational physics, considering both conventional and quantum computing computational platforms. The variational quantum eigensolver algorithm aims to prepare the ground state of a Hamiltonian exploiting parametrized quantum circuits that may offer an advantage compared to classical trial states used,...

The nonlinear Schr{\"o}dinger equation (NLSE) is one of the most important physical model in optical fiber theory for explaining changes in optical soliton growth. Due to the large range of opportunities for ultrafast signal routing systems and brief pulses of light in communications, optical soliton transmission in nonlinear fibers is currently a...

Numerical simulations of streamer propagation involving photoionization are presented, utilizing an ANSYS Fluent implementation that employs unstructured meshes and automatic mesh refinement. Two approximate methods for radiative transfer, used to handle computation of the photoionization source terms, are compared: the Eddington approximation and...

In this paper, the dynamical behavior of the coupled Higgs equation (CHE) is explored analytically, which explains the interaction between mesons and electrons to characterize smooth changes in states of a particle. In this paper, a collection of efficient analytical procedures is employed to develop a variety of intriguing travelling wave structur...

Metacognition is influenced the creative thinking process in Computational Physics courses. The Computational Physics is needed in the business and industrial. However, Computational Physics courses are less attractive for students. The challenge in learning Computational Physics is how to help students effectively develop creative and computationa...

In recent years, computing has become an important part of the way we teach and learn physics. Teachers, both at high school and college levels, now use computational activities in many of their courses. Physics departments are offering specialized courses and degrees in computational physics. And many countries are adding programming or computatio...

Air pollution is a pressing concern that the entire world is striving to combat. Among air pollutants, particulate matter poses a significant threat to human health. The Sustainable Development Goals (SGD3, SGD7 and SGD11) include initiatives to address air pollution. Two innovative methods are proposed in this research to predict the PM2.5 concent...

This paper introduces a new and challenging Hidden Intention Discovery (HID) task. Unlike existing intention recognition tasks, which are based on obvious visual representations to identify common intentions for normal behavior, HID focuses on discovering hidden intentions when humans try to hide their intentions for abnormal behavior. HID presents...

In recent years, in the category of numerical analysis, the meshless method has witnessed a research boom to free engineers and scientists from the difficult task of mesh generation and to reduce mesh sensitivity of solutions. Meshless methods include the kernel methods, the moving least square method, the radial basis functions, etc, such as the w...

While the recently proposed TENO (targeted essentially non-oscillatory) schemes [Fu et al., Journal of Computational Physics 305 (2016): 333-359] exhibit better performance than the classical WENO (weighted essentially non-oscillatory) schemes with the same accuracy order, there is still a room for further improvement, e.g., the physical discontinu...

Boltzmann distributions over lattices are pervasive in Computational Physics. Sampling them becomes increasingly difficult with the increase in the number of dimensions, especially near critical regions, e.g., phase transitions or continuum limits. Conditional generative models are emerging as promising tools for sampling in critical regions. When...

The influence of single, double absorber layers based on perovskite solar cells has attracted considerable attention of researchers in the last few years; according to their promising output parameters such as short-circuit current (JSC), open-circuit voltage (VOC), fill factor (FF) and power conversion efficiency (PCE). The present work makes unde...

У статті розглядаються засоби та методи забезпечення наочності при викладанні фізики з урахуванням суттєвого скорочення навчального часу для вивчення у вищому військовому навчальному закладі (ВВНЗ). Порівнюються два основних способи у вирішенні цієї проблеми: традиційний підхід та застосування сучасних комунікативно-інформаційних технологій.

Despite the successes of machine learning methods in physical sciences, the prediction of the Hamiltonian, and thus the electronic properties, is still unsatisfactory. Based on graph neural network architecture, we present an extendable neural network model to determine the Hamiltonian from ab initio data, with only local atomic structures as input...

The fluid-like material motion of fluids, gasses, and solids is commonly termed hydrodynamics. Lagrangian hydrodynamic methods solve the governing physics equations for compressible material dynamics on a mesh that moves with the deforming material. For some applications, it is reasonable to assume rotational symmetry so simulations can be performe...

A bstract We give the first numerical calculation of the spectrum of the Laplacian acting on bundle-valued forms on a Calabi-Yau three-fold. Specifically, we show how to compute the approximate eigenvalues and eigenmodes of the Dolbeault Laplacian acting on bundle-valued ( p , q )-forms on Kähler manifolds. We restrict our attention to line bundles...

The research in Artificial Intelligence methods with potential applications in science has become an essential task in the scientific community last years. Physics Informed Neural Networks (PINNs) is one of this methods and represent a contemporary technique that is based on the fundamentals of neural networks to solve differential equations. These...

High-order iterative techniques without derivatives for multiple roots have wide-ranging applications in the following: optimization tasks, where the objective function lacks explicit derivatives or is computationally expensive to evaluate; engineering; design finance; data science; and computational physics. The versatility and robustness of deriv...

A three‐stage simulation is used to explore the chemical influence of low altitude (50 km) sprite streamers on the atmosphere, including the chemical trail after the streamer has faded away. In the first stage (streamer phase) a 2D electrodynamical streamer model quantifies the generation of NOx and N2O, and the removal of ozone (O3) by a downward...

In this paper, we demonstrate a molecular system for the first active self-assembly linear DNA polymer that exhibits programmable molecular exponential growth in real time, also the first to implement “internal” parallel insertion that does not rely on adding successive layers to “external” edges for growth. Approaches like this can produce enhance...

How to model three bodies interacting? The solution is to computationally model the system using fluid mechanics. The drifting of infinite points of a fluid surface can be modeled by fluid mechanics to this solves the 3 and n-body problems. Space time is a fluid with ripples and solidity. Ergo, computational physics using fluid flow can be broken d...

Physics-informed deep learning has recently emerged as an effective tool for leveraging both observational data and available physical laws. Physics-informed neural networks (PINNs) and deep operator networks (DeepONets) are two such models. The former encodes the physical laws via the automatic differentiation, while the latter learns the hidden p...

Computational Physics

Improving the predictive capability and computational cost of dynamical models is often at the heart of augmenting computational physics with machine learning (ML). However, most learning results are limited in interpretability and generalization over different computational grid resolutions, initial and boundary conditions, domain geometries, and...

This paper introduces discrete-holomorphic Perfectly Matched Layers (PMLs) specifically designed for high-order finite difference (FD) discretizations of the scalar wave equation. In contrast to standard PDE-based PMLs, the proposed method achieves the remarkable outcome of completely eliminating numerical reflections at the PML interface, in pract...

Ion cyclotron radio frequency range (ICRF) power plays an important role in heating and current drive in fusion devices. However, experiments show that in the ICRF regime there is a formation of a radio frequency (RF) sheath at the material and antenna boundaries that influences sputtering and power dissipation. Given the size of the sheath relativ...

The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction–diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a challenging problem. This paper introduces an approach to the prediction of Turing parameter values from obser...

Microwave ablation is a procedure for treating various types of cancers during which a small needle-like probe is inserted inside the tumor, which delivers microwave energy, causes tissue heating, and effectively produces necrosis of the tumor tissue. Mathematical models of microwave ablation involve the modeling of multiple physical phenomena that...

This work extends, to moving geometries, the immersed boundary method based on volume penalization and selective frequency damping approach [J. Kou, E. Ferrer, A combined volume penalization/selective frequency damping approach for immersed boundary methods applied to high-order schemes, Journal of Computational Physics (2023)]. To do so, the numer...

The aim of this study was to analyze the operation of a computational physics course in preservice physics teacher education. The lecture plans of 14 universities were analyzed, and interviews and questionnaires were conducted with five professors. The main research results were as follows. First, the purposes of computational physics courses could...

In this work, a new family of Time-Accurate and highly-Stable Explicit (TASE) operators for the numerical solution of stiff Initial Value Problems (IVPs) that extends that of M. Bassenne, L. Fu and A. Mani [Journal of Computational Physics 424 (2021): 109847] is proposed. The new TASE operators depend on the inverse of a single matrix (thus called...

In this paper, we propose a type of tensor-neural-network-based machine learning method to compute multi-eigenpairs of high dimensional eigenvalue problems without Monte-Carlo procedure. Solving multi-eigenvalues and their corresponding eigenfunctions is one of the basic tasks in mathematical and computational physics. With the help of tensor neura...

Background Currently, in the scientific literature there is a great interest on the study of strategies to implement patient-centered care. One of the main tools for this is the therapeutic relationship. Some studies suggest that the perception of the environment in which the treatment takes place can influence the perception of its quality, but th...

This advanced short course is primarily designed for Master and PhD students in applied mathematics, scientific computing, computational physics, and computational mechanics. Summary: This course on advanced numerical methods for the modeling of complex environmental processes consists of a structured intensive 2.5 week program of 80 hours of theo...

The integration of technology and new tools in engineering education has created opportunities for the advancement of geotechnical engineering education (GEE). Technology-enhanced learning (TEL) has been implemented in GEE by many educators, and its impacts have been studied by different researchers. This review paper presents a comprehensive analy...

The relevance of the application of hydraulic thruster technology is determined by the technological limitations of drilling both vertical and horizontal wells. The existing experimental studies confirm the effectiveness of the technology, but its widespread implementation is hindered by the lack of scientific foundations for its operation in combi...

This article reformulates the theory of computable physical models, previously introduced by the author, as a branch of applied model theory in first-order logic. It provides a semantic approach to the philosophy of science that incorporates aspects of operationalism and Popper’s degrees of falsifiability.

The TENO-family schemes [Fu et al., Journal of Computational Physics 305 (2016): 333-359] have been demonstrated to perform well for compressible gas dynamics and turbulent flow predictions on structured meshes. However, the extension of the TENO schemes to unstructured meshes is non-trivial and challenging, particularly when the multiple design ob...

Compared to other computational physics areas such as codes for general computational fluid dynamics (CFD), the documentation of verification methods for plasma fluid codes remains under developed. Current analytical solutions for plasma are often highly limited in terms of testing highly coupled physics, due to the harsh assumptions needed to deri...

Beyond automatic differentiation, there are several other techniques and approaches for computing gradients or derivatives of mathematical functions. Some of these include: 1. Symbolic differentiation: This involves using mathematical techniques to derive the algebraic expressions for the derivatives of a function. Symbolic differentiation can be m...

Collision detection is a fundamental computational problem in various domains, such as robotics, computational physics, and computer graphics. In general, collision detection is tackled as a computational geometry problem, with the so-called Gilbert, Johnson, and Keerthi (GJK) algorithm being the most adopted solution nowadays. While introduced in...

Identifying transition paths between distant regions of an energy landscape is an important goal in statistical and computational physics, with relevant applications in evolutionary biology. We here consider the case of Potts-like landscapes, in which configurations are made of a large number of categorical variables, taking A distinct values. We s...

Gibbs samplers are preeminent Markov chain Monte Carlo algorithms used in computational physics and statistical computing. Yet, their most fundamental properties, such as relations between convergence characteristics of their various versions, are not well understood. In this paper we prove the solidarity of their spectral gaps: if any of the rando...

This dissertation is related to the research areas of computational physics and numerical modeling of combustion. The study intends to determine the ignition characteristics of dual-fuel (DF) and tri-fuel (TF) sprays when a high-reactivity fuel spray (n-dodecane) is mixed with a low-reactivity fuel (methane/hydrogen/or its blends) and an oxidizer/E...

Mathematics is a subject that is difficult to understand, because of abstract concepts. Integrating physics which is a subject that uses mathematics as a tool, will make mathematics more concrete. This research is an application of GeoGebra for integrated physics and mathematics instruction. By selecting mechanical energy lessons in physics, relati...

Computation is intertwined with essentially all aspects of physics research and is invaluable for physicists’ careers. Despite its disciplinary importance, integration of computation into physics education remains a challenge and, moreover, has tended to be constructed narrowly as a route to solving physics problems. Here, we broaden Physics Educat...

Differentiable programming has facilitated numerous methodological advances in scientific computing. Physics engines supporting automatic differentiation have simpler code, accelerating the development process and reducing the maintenance burden. Furthermore, fully differentiable simulation tools enable direct evaluation of challenging derivatives—...

The ability to efficiently solve topology optimization problems is of great importance for many practical applications. Hence, there is a demand for efficient solution algorithms. In this paper, we propose novel quasi-Newton methods for solving PDE-constrained topology optimization problems. Our approach is based on and extends the popular solution...

Abstrak Komputasi astronomi adalah cabang yang sangat penting di era sekarang ini, di mana fisikawan atau peneliti dapat menggunakan komputer untuk memproses statistik dalam fisika astronomi. peneliti dapat mengolah data abstrak dari data mentah dan dapat mengubah data menjadi visualisasi data. Fisika komputasi astronomi adalah metode yang canggih...

Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations. Computational physics astronomy is a sophisticated and well-established method, this br...

ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numerical algorithms. Specifically, we examine the capability of GhatGPT for generating codes for numerical algorithms in different programming languages, for debugging and impr...

The use of information–theoretical methodologies to assess graph-based systems has received a significant amount of attention. Evaluating a graph’s structural information content is a classic issue in fields such as cybernetics, pattern recognition, mathematical chemistry, and computational physics. Therefore, conventional methods for determining a...

We have developed a simulation technique that uses non-linear finite element analysis and elastic fracture mechanics to compute physically plausible motion for three-dimensional, solid objects as they break, crack, or tear. When these objects deform beyond their mechanical limits, the system automatically determines where fractures should begin and...

High-fidelity scale-resolving computational fluid dynamics (CFD) simulations may provide a path towards predictive capabilities for many engineering-relevant applications, in particular those involving the interaction between multiple physics such as turbulent multi-component reacting flows. However, their routine use is hindered by their cost. The...

Deep neural network (DNN) and auto differentiation have been widely used in computational physics to solve variational problems. When DNN is used to represent the wave function to solve quantum many-body problems using variational optimization, various physical constraints have to be injected into the neural network by construction, to increase the...

We discuss the particle-in-cell (PIC) method, which is one of the most widely used approaches for the kinetic description of plasmas. The positions and velocities of the charged particles take continuous values in phase space, and spatial macroscopic quantities, such as the charge density and self-generated electric fields, are calculated at discre...

Heavy carbon (C) doping is of great significance for semi-insulating GaN in power electronics. However, the doping behaviors, especially the atomic configurations and related self-compensation mechanisms, are still under debate. Here, with the formation energy as the input parameter, the concentrations of C defects with different atomic configurati...

Physics-informed neural networks (PINNs) are gaining popularity as powerful tools for solving nonlinear Partial Differential Equations (PDEs) through Deep Learning. PINNs are trained by incorporating physical laws as soft constraints in the loss function. Such an approach is effective for trivial equations, but fails in solving various classes of m...

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. Howeve...

A common problem that affects simulations of complex systems within the computational physics and chemistry communities is the so-called sampling problem or rare event problem where proper sampling of energy landscapes is impeded by the presences of high kinetic barriers that hinder transitions between metastable states on typical simulation time s...

This essay traces the history of early molecular dynamics simulations, specifically exploring the development of SHAKE, a constraint-based technique devised in 1976 by Jean-Paul Ryckaert, Giovanni Ciccotti and the late Herman Berendsen at CECAM (Centre Européen de Calcul Atomique et Moléculaire). The work of the three scientists proved to be instru...

Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, and analyzing these data. While error-controlled lossy compressors are effective in significantly reducing data volumes and efficiently developing databases for multiple scientific applications, they mainly support...

Classical problems in computational physics such as data-driven forecasting and signal reconstruction from sparse sensors have recently seen an explosion in deep neural network (DNN) based algorithmic approaches. However, most DNN models do not provide uncertainty estimates, which are crucial for establishing the trustworthiness of these techniques...