Alex Gezerlis · Cambridge University Press eBooks · 2023
Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
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W. Brian Lane, Terrie M. Galanti, X. L. Rozas · 2023
Integrating computational thinking (CT) into high school physics requires physics teachers to develop specialized pedagogical content knowledge of computing and content integration. We describe this as a re-novicing experience, in which teachers build and act upon knowledge at the intersection of physics, computation, and pedagogy. We observed this re-novicing in a Python workshop for physics teachers (n = 23) from three countries. Teachers participated in computational learning activities in Jupyter Notebooks with the goal of developing their capacity to integrate Python in physics applications. In this exploratory study, we describe a framework for integrated CT professional learning and report teacher responses to a post-workshop survey about their motivations for participating in this re-novicing process. Their motivations included needing to learn essential programming features and an intrinsic motivation to transform how they teach physics. These findings suggest that professional learning for CT integration needs to interweave computing content with pedagogical applications.
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<ns0:vcard>BEGIN:vCardVERSION:3.0FN:Rubin LandauTITLE:ORG:Oregon State UniversityADR;TYPE=WORK,POSTAL,PARCEL:;;;;;Other;;USTEL;TYPE=:+TEL;TYPE=:+TEL;TYPE=FAX:+EMAIL;TYPE=INTERNET,Work:rubin@physics.orst.eduEMAIL;TYPE=INTERNET,:END:vCard</ns0:vcard>, <ns0:vcard>BEGIN:vCardVERSION:3.0FN:Manuel J. PaezORG:Universidad de AntioquiaADR;TYPE=WORK,POSTAL,PARCEL:;;;;Medellin;;;COEND:vCard</ns0:vcard> · The Physics Video Demonstration Database (Cornell University)
This website hosts resources supporting the textbook, Computational Physics 4th Ed.: Problem Solving with Python authored by Rubin H Landau, Manuel J Paez, and Cristian Bordeianu, with contributions by Guangliang He. ISBN: 978-3-527-41425-3 Materials available include python code for the text and example problems, lecture slides, quizzes, and video lectures by the primary author. Topics covered are the basics of scientific computation, data science, and applications to physics systems.
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Maksym Pavlenko, Лілія Павленко, Y V Iotov et al. · Journal of Physics Conference Series · 2025
Abstract The study explores the integration of programming into the professional development of prospective physics educators. The study emphasizes the development of practical skills in computational methods and data visualization. The course is structured into 13 lectures and 12 practical sessions. The lectures cover foundational Python programming, visualization of physical phenomena, and modeling of school physics topics. The practical sessions focus on the application of these skills. The study underscores the contemporary relevance of integrating Python, a widely adopted programming language in science and education, into the pedagogical training of physics teachers. The course aligns with STEM education principles, enabling participants to model, analyze, and visualize physical phenomena, thus bridging theoretical physics concepts and real-world applications. This integration equips future educators with critical digital competencies to meet the demands of modern educational practices. The study uses an adapted Course Experience Questionnaire (CEQ) to assess student satisfaction and the effectiveness of the course. The findings indicate a high level of satisfaction in various domains, including teaching quality, clarity of objectives, and practical relevance. However, the study also identified challenges, such as the need for additional support for students without previous programming experience. This research contributes to the broader discourse on educational innovation by demonstrating the feasibility and benefits of incorporating computational tools like Python into physics education. The study offers actionable information for curriculum development and underscores the potential to cultivate computational literacy among prospective educators.
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W. Brian Lane, Terrie M. Galanti, X. L. Rozas · Journal for STEM Education Research · 2023
Abstract Integrating computational thinking (CT) into STEM disciplines requires secondary teachers to develop their pedagogical content knowledge of computing and content integration. Experienced teachers who choose to integrate CT in their secondary STEM courses may struggle in the same ways as novice teachers as they learn about programming and its potential use within their content areas. This study describes these potential struggles as teacher re-novicing in the context of high school physics. The research team facilitated a week-long computing integration workshop for physics teachers ( n = 24) from three countries. The teachers engaged with computational learning activities in Jupyter Notebooks with the goal of developing their capacity to integrate Python in physics applications. Qualitative analysis of teacher surveys supported our theorization of a pathway of CT integration knowledge development. We describe these professional learning needs in four illustrative cases, building a grounded theory for teacher re-novicing as a pathway beginning with computing knowledge, moving through physics applications of computing, and arriving at pedagogical knowledge for physics-CT integration.
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Jinshuai Bai, Hyogu Jeong, Chanaka Batuwatta-Gamage et al. · arXiv (Cornell University) · 2022
Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-based computational solid mechanics. Besides, two prevailingly used physics-informed loss functions for PINN-based computational solid mechanics are summarised. Moreover, numerical examples ranging from 1D to 3D solid problems are presented to show the performance of PINN-based computational solid mechanics. The programs are built via Python coding language and TensorFlow library with step-by-step explanations. It is worth highlighting that PINN-based computational mechanics is easy to implement and can be extended for more challenging applications. This work aims to help the researchers who are interested in the PINN-based solid mechanics solver to have a clear insight into this emerging area. The programs for all the numerical examples presented in this work are available on https://github.com/JinshuaiBai/PINN_Comp_Mech.
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Dimitrios Mitsotakis · 2023
This textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject, as well as advantages and pitfalls of classical numerical methods alongside with computer code and experiments in Python. Each chapter closes with modern applications in physics, engineering, and computer science. Features: No previous experience in Python is required. Includes simplified computer code for fast-paced learning and transferable skills development. Includes practical problems ideal for project assignments and distance learning. Presents both intuitive and rigorous faces of modern scientific computing. Provides an introduction to neural networks and machine learning.
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Ufuk Karaoğlan, Yakup Kutlu · Journal of Intelligent Systems with Applications · 2025
This study examines AI-based modeling for multi-physics simulations of rotary mechanisms, focusing on the complex interactions between mechanical, thermal, and fluid domains. While conventional simulation methods are often limited by high computational costs and long processing times, this study evaluates data-driven and physics-aware alternatives—specifically Artificial Neural Networks (ANN), Physics-Informed Neural Networks (PINN), and Graph Neural Networks (GNN). Due to hardware and time constraints, rather than large-scale data generation, the research establishes a conceptual and methodological framework for integrating simulation-aware AI into rotary systems. To assess practical implementation challenges, a Monte Carlo-based feasibility analysis was developed in Python; this analysis estimated a success probability of approximately 0.77% for training these models under current resource limitations. Ultimately, this study contributes to the literature by providing a structured roadmap for AI-supported multi-physics modeling and offering practical guidance for engineering applications operating under significant computational constraints.
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Adriaan Graas, Willem Jan Palenstijn, Ben van Werkhoven et al. · Applied Mathematics for Modern Challenges · 2024
New computed tomography (CT) algorithms are commonly developed in high-level programming languages, such as Python or MATLAB, while low-level languages are used to support their computation-intensive operations. In the past decade, graphics processing units (GPUs) have become the de-facto standard for large parallel computations in areas such as computational imaging, image processing, and machine learning. Our fast-and-flexible CT reconstruction software, ASTRA Toolbox, therefore already implemented tomographic projectors, i.e., the core computational operations modeling the X-ray physics, using NVIDIA CUDA (Compute Unified Device Architecture), a low-level platform for computation on GPUs. However, the Python-C++ language barrier prevents high-level Python users from modifying these low-level projectors, and, as a consequence, research into new tomographic algorithms is more complex and time-consuming than necessary. With the ASTRA KernelKit, we lifted tomographic projectors to Python and leveraged CuPy, a numerical software like NumPy and SciPy that exposes CUDA to Python, to obtain a fine-grained control over their efficiency and implementation. In this article, we introduced our software and illustrated its importance for high-performance and data-driven applications using examples from deep learning, real-time X-ray CT, and kernel tuning.
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Andrew Knapp, Dubravko Horvat, Diana Šaponja- Milutinović et al. · International Journal of Innovative Research in Multidisciplinary Education · 2025
Python programming language is an increasingly widespread tool used for solving general computational problems in a variety of applications. In 2018, at the Zagreb University of Applied Sciences in Croatia, we introduced laboratory-computer exercises to a physics course in which Python was used to solve problems and to process the results of measurements. Students of Information Technology who attended our course had already completed a course in Programming basics in the previous semester. Our objective was to encourage IT students to apply the programming skills they had acquired to solve problems in other subjects. Before introducing Python, we used to solve problems by explaining the physical framework of the problem, making a sketch, writing down formulae and solving them on the blackboard. With the new approach, we additionally analyze problems using coresponding Python programs which were designed to provide further insights into the problem through graphical representation and additional visualization. This proved to be particularly beneficial during the 2020 lockdown, when we transitioned to online classes. The purpose of this paper is to demonstrate several examples of how we solved problems using the Python programming language and to analyse the reactions of students to this new way of teaching physics.
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Parcollet, Olivier, Ferrero, Michel, Ayral, Thomas et al. · arXiv (Cornell University) · 2015
We present the TRIQS library, a Toolbox for Research on Interacting Quantum Systems. It is an open-source, computational physics library providing a framework for the quick development of applications in the field of many-body quantum physics, and in particular, strongly-correlated electronic systems. It supplies components to develop codes in a modern, concise and efficient way: e.g. Green's function containers, a generic Monte Carlo class, and simple interfaces to HDF5. TRIQS is a C++/Python library that can be used from either language. It is distributed under the GNU General Public License (GPLv3). State-of-the-art applications based on the library, such as modern quantum many-body solvers and interfaces between density-functional-theory codes and dynamical mean-field theory (DMFT) codes are distributed along with it.
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Nadia Ali Abbas Almosa, Ahmed Sabah Al-Jilawi · AIP conference proceedings · 2023
In recent years, mathematical modeling has played a key role in many life applications such as computer science, physics, chemistry, and genetics. Actually, in this paper, our focus is on the classifications and the importance of mathematical programming and its applications in health problems especially the Mathematical Modeling of COVID_19. According to the era of the Corona pandemic, it has been using mathematical equations to employ mathematical programming in epidemics and the mechanism of spreading in urban areas. The solution of the problem is presented in two directions; the first was by graphic representation and the other by using computational software via the Python language.
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L. Anderlini, M. Barbetti · 2022
Machine Learning plays a major role in Computational Physics providing arbitrarily good approximations of arbitrarily complex functions, for example with Artificial Neural Networks and Boosted Decision Trees. Unfortunately, the integration of Machine Learning models trained with Python frameworks in production code, often developed in C, C++, or FORTRAN, is notoriously a complicated task. We present scikinC, a transpiler for scikit-learn and Keras models into plain C functions, intended to be compiled into shared objects and linked to other applications. An example of application to the parametrization of a detector is discussed.
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Jimmi Ari Duri, Yuniana Cahyaningrum, S. Asif · International Journal of Applied Mathematics and Computing. · 2025
Integral equations are essential tools in applied mathematics, with wide-ranging applications in fields such as physics, engineering, and finance. However, solving these equations presents significant challenges, particularly when dealing with complex, high-dimensional, or singular problems. Traditional methods, such as manual analytical techniques or direct numerical approaches, often struggle with computational efficiency, especially for large-scale systems, and may not be suitable for handling ill-conditioned problems. This study aims to develop an efficient numerical method for solving integral equations by combining adaptive quadrature techniques with Python-based iterative solvers. The adaptive quadrature method adjusts the step size dynamically based on error estimates, ensuring high accuracy even in the presence of singularities or near-singularities, which are common in many real-world problems. The iterative solver, based on Krylov subspace methods, enhances computational efficiency by reducing memory usage and improving the convergence speed of the solution. By using these techniques together, the proposed method significantly improves the computational time required to solve large-scale and complex systems of integral equations, while maintaining satisfactory accuracy. The results demonstrate that the adaptive quadrature technique, when combined with the Python-based iterative solver, offers a substantial advantage in both speed and precision compared to traditional methods. The proposed method is especially effective in handling complex, high-dimensional systems and ill-conditioned problems, making it a powerful tool for applied mathematics, physics, and engineering applications. In conclusion, this study presents a robust and efficient approach for solving integral equations, with potential for future research in solving non-linear and multi-dimensional integral equations.
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ugwczrjtu · Zenodo (CERN European Organization for Nuclear Research) · 2024
&lt;pre&gt;&lt;code&gt;\n&lt;p&gt;&lt;strong&gt;computational physics mark newman pdf&lt;/strong&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Rating: 4.9 / 5 (1602 votes)&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Downloads: 36797&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n &lt;p&gt;= = = = = \n&lt;strong&gt;&lt;a href="https://tds11111.com/21Nr9y?keyword=computational physics mark newman pdf" target="_blank"&gt;CLICK HERE TO DOWNLOAD&lt;/a&gt;&lt;/strong&gt;\n = = = = = &lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;br&gt;Prerequisites : Calculate the position of a ball dropped from a tower: Check two integers to ensure one is even and the other odd and techniques of computational physics, the physical and mathematical in-sights that allow us to perform accurate calculations of physical quantities on the computer. One of the most basic but also most important applications of computers in physics is the evaluation of integrals and derivatives. A well-known quantum mechanics problem involves a particle of mass m that encounters a one-dimensional potential step: The particle with initial kinetic energy E and wavefactor k1 = 2mE− −−−√ /ℏ k=m E ℏ enters from the left and encounters a sudden jump in potential energy of heightComputational Physics with Python. The Python programming language is an excellent choice for learning, teaching, or doing computational physics. Computational Physics PDF Tags Online PDF Computational Physics, Read PDF Computational Physics, Full PDF Computational Physics, All Ebook Computational Physics, PDF and EPUB Computational Physics, PDF ePub Mobi Computational Physics, Reading PDF Computational Physics, Book PDF Solutions Manuals are available for thousands of the most popular college and high school textbooks in subjects such as Math, Science (Physics, Chemistry, Biology), Engineering (Mechanical, Electrical, Civil), Business and more. By rearranging the equation above for dx/dt dx dt and then integrating with respect to t t fromtoTT, we show that the period T T is given by: T = 8m−−−√ ∫adx : Calculate the position of a ball dropped from a tower: Check two integers to ensure one is even and the other odd The ideas are illustrated with examples drawn from various branches of physics, including classical mechanics, special relativity, and quantum physics. Numerical evalua- Computational physics is concerned with performing computer calculations and simulations for solving physical problems. It includes a range of features tailored for scientific computing, including features for It is a well-designed, modern programming language that is simultaneously easy to learn and very powerful. ChapterGraphics and visualization – This chapter gives an introduction to some of Pythons features for making scientific graphics, including graphs, density plots, and 3D visualizations of physical This course is a very hands-on course which has two main objectivesLearning basic methods, tools, and techniques of computational physicsDeveloping practical computational problem-solving skills. The course will have a project as well as biweekly assignments. Although computer memory and pro-cessor performance have increased dramatically over the last two ades, most physical problems are too complicated to be solved without approximations to the Computational Physics. Understanding Computational Physics homework has never been easier than with Chegg Study Exercise Quantum potential step ¶.&lt;/p&gt;&lt;/code&gt;&lt;/pre&gt;
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O'Donnell, Erin, Rasmussen, Melissa · Digital Commons - USU (Utah State University) · 2021
The computer-based curriculum for undergraduate physics students before 2019 struggled to stay current and applicable. When the course was created in the early 2000s, MathCad was used daily by many physicists. As computer’s and their computational abilities have grown exponentially, so have programming languages and applications. Now in the late 2010s/early 2020s, Excel, Python, and Mathematica are some of the most common computer appliques. To address this, we created toolkits to give undergraduate physicists the experience for future classes, careers, and graduate school. This paper focuses on the process for creating the Python toolkit and overall class setup. Run on Google Colabratory, no external programs need to be downloaded making the toolkit accessible to all with internet access. The toolkits the main points are: Functions, Loops, Downloading Data, etc. We based examples and problems on physics the students previously learned in their prerequisite classes (Snell’s law, star classification, kinematics). After employing the toolkit in PHYS 2500: Computer Methods at Utah State University for Fall 2019 and Fall 2020, student knowledge on coding, and coding physics, significantly improved and overall satisfactory of the course improved.
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Germán Filippini; Centro Internacional de Métodos Computacionales en IngenierÃa (CIMEC) INTEC (CONICET-UNL), Lisandro Dalcin; Centro Internacional de Métodos Computacionales en IngenierÃa (CIMEC) INTEC (CONICET-UNL), Norberto Nigro; Centro Internacional de Métodos Computacionales en IngenierÃa (CIMEC) INTEC (CONICET-UNL) et al. · Mecánica Computacional (Asociación Argentina de Mecánica Computacional) · 2009
Fluid structure interaction (FSI) involving rigid bodies contains three main problems to be solved, the computational fluid dynamics (CFD), the computational mesh dynamics (CMD) and the multi-body dynamics (MBD). Python is used as a glue language capable of connecting this three main problems in a high-level, interactive and productive environment. This interaction is implemented in PETSc-FEM code(http://www.cimec.org.ar/petscfem) which is a parallel multi-physics finite element based on PETSc. PETSc is a suite of data structures and routines for the scalable solution of scientific applications modeled by partial differential equations. It employs the MPI standard for all message-passing communication. PETSc for Python (petsc4py) are Python bindings for PETSc used in this work.\nA stabilized ALE (Arbitrary Lagrangian-Eulerian) formulation is used to solve the incompressible laminar Navier Stokes equations in a moving grid. The mesh dynamics may be solved in general by a global optimization strategy, however, in some special cases, a simple ad-hoc procedure may be adopted. For each subproblems a second order accurate in\ntime scheme is adopted. Results for vortex-induced vibrations (VIV), galloping and flutter of some numerical examples at low Reynolds number are presented.
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de La Fournière, Brieuc · Zenodo (CERN European Organization for Nuclear Research) · 2025
We present a highly speculative theoretical framework extending PSI7 seven-dimensional information processing dynamics to string theory, proposing "living physics" where strings actively explore possibility space through information-theoretic coupling. The unified Lagrangian incorporates six coupled dynamics (kinetic, thermal, predictive, entropic, curiosity, exploration) into classical string action, operating on manifold M₇ = ℝ⁴ ⊗ I ⊗ C ⊗ M. Computational validation demonstrates consciousness emergence (CI = 6.032, above threshold 0.6), fractal cosmic structures (d = 1.65 ± 0.18), and electromagnetic resonances at 1420 MHz/2.1 GHz. The framework produces testable predictions: observer coupling effects (κₑ > 0.5), phase transitions at consciousness thresholds, and t^(2/3) information accumulation scaling. Critical limitations: This work represents computational modeling rather than empirical physics. Results are mathematical artifacts requiring extensive validation. The framework makes no claims about biological consciousness or subjective experience. Applications: Cosmic intelligence detection, observer-reality coupling experiments, information-theoretic cosmology. Provides mathematical foundation for studying consciousness as potential cosmic force. Repository: Complete Python implementation available with reproducible results (internal consistency score 0.87 ± 0.08).
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Dobler, Gregory · Zenodo (CERN European Organization for Nuclear Research) · 2022
The use of computational algorithms, implemented on a computer, to extract information from data has a history that dates back to at least the middle of the 20th century. However, the confluence of three recent developments has led to rapid advancements in this methodology over the past 15-20 years: the advent of the era of large datasets in which massive of amounts of data can be collected, stored, and accessed efficiently; the development of computational algorithms that can perform classification and prediction to high degrees of accuracy across a variety of applied situations; and broad access to the computational power of modern computing systems that allow for the building of complex models of phenomenology in diverse domains. In this talk I will describe the basic fundamentals of Machine Learning (ML), how ML is used to extract information from data, the potential pitfalls to avoid when using ML in a variety of applications, the relationship between ML and what is currently commonly referred to as Artificial Intelligence (AI), and the transferability of ML from physics-based to non physics-based problems. The second half of this presentation will consist of a live demo applying ML to a physics application in the Python coding language using publicly available tools.
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Murali Krishna Pasupuleti · International Journal of Academic and Industrial Research Innovations(IJAIRI) · 2023
Abstract: This study addresses the lack of trustworthy uncertainty quantification in physics-informed neural operators used to approximate solution operators of partial differential equations. A Python framework is proposed in which Fourier- and DeepONet-style backbones are trained with physics residuals, energy-consistency penalties, and boundary/initial condition constraints, while a verification layer delivers instance-wise error guarantees. The method integrates global Lipschitz constant estimation, interval bound propagation over input function spaces, and distribution-free conformal calibration to produce certified prediction sets. Experiments on synthetic PDE benchmarks (elliptic, parabolic, and advection–diffusion systems) evaluate accuracy and coverage using relative L2 error, energy norm error, certified upper bounds, and empirical coverage at target levels. The verified neural operator achieves competitive accuracy with 20–40% tighter certified bounds and coverage within ±2% of nominal levels, at inference latencies compatible with real-time surrogates. Implications include safer deployment of operator learners in digital twins and scientific simulators; limitations involve conservatism under highly non-linear shocks and computational overhead for tight Lipschitz estimates. This matters for applications demanding reliability—design optimization, control, and risk-aware decision-making—where formal guarantees complement data-driven performance. The paper details the training objective, verification pipeline, ablations across backbones and norms, and guidelines for integrating certified operators into Python-based PDE workflows. Keywords neural operators, physics-informed learning, verified error bounds, conformal calibration, Lipschitz certification, interval bound propagation, Fourier neural operator, DeepONet, PDE surrogates, uncertainty quantification, reliability, Python
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