Computational Physics and Python Applications

Últimos artículos

• Training and Inference Dynamics of PLDR-LLMs: Row-Map Collapse, Renormalization, and Predictive Reduction

  Burc Gokden · 30 de septiembre de 2026

  This monograph develops a unified account of training and inference in Power Law Decoder Representation language models (PLDR-LLMs). Exact finite work identities decompose changes in the absolute energy of the row-centered learned map into parameter contributions, signed interactions, and numerical …

• Port-Hamiltonian Latent Deliberation: Mitigating the Deliberation Drift Cliff in Test-Time Compute Scaling

  Zeyu Jia (School of Biomedical Engineering,Technology, Tianjin Medical University, Medical School, Tianjin University) · 30 de septiembre de 2026

  Test-time compute scaling has emerged as a cornerstone of advanced machine reasoning, yet performing iterative deliberation directly within continuous latent representation spaces reveals a catastrophic pathology: the Deliberation Drift Cliff. While unconstrained recurrent latent models achieve init…

• FLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution

  Pengyu Li, Renjie Tong, Xuanlue Jiang, Jianmin Li, Yuanyuan Zhou · 29 de septiembre de 2026

  Long-horizon micromagnetic simulation remains expensive because conventional and learned solvers typically propagate Landau--Lifshitz--Gilbert (LLG) dynamics step by step. Existing learned approaches generally retain stepwise integration or model deterministic evolution, leaving full-field, direct-h…

• MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception

  Yuhao Li, Louie Hong Yao, Tianyi Shi, Hanqun Cao, Hongxia Hao, Zhen Zhao, Shengchao Liu · 29 de septiembre de 2026

  Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models capture such structure broadly through physics-motivated dynamical formulations or learning-motivated architectures. The former provide stronger prior…

• Phase Space Attention:A Hairer Lift Circumvents the Single-Layer Induction Obstruction

  Kingsuk Maitra, Shagun Sood Morteza Hosseini, Suman Gunnala, Vikram Gupta · 29 de septiembre de 2026

  We circumvent the Sanford-Hsu-Telgarsky (SHT) single-layer induction obstruction within a linear, one-step, causal, bilinear, symplectically consistent design class on the post-RoPE substrate, by lifting attention onto a symplectic phase space, mirroring Hairer's lift of Stormer-Verlet. The lift exi…

• Retrainable physics-integrated neural differentiable modeling of sintering across material systems

  Zeping Chen, Ani Aprahamian, Khachatur V. Manukyan, Tengfei Luo · 28 de septiembre de 2026

  Sintering is widely used to manufacture ceramics, but coupled densification and grain growth, material-dependent kinetics, and sparse measurements complicate predictive modeling and process design. We present Sinter-PiNDiff, a retrainable physics-integrated neural differentiable framework for predic…

• A Discrepancy-Based Perspective on Dataset Condensation

  Tong Chen, Raghavendra Selvan · 24 de septiembre de 2026

  Given a dataset of finitely many elements $\mathcal{T} = \{\mathbf{x}_i\}_{i = 1}^N$, the goal of dataset condensation (DC) is to construct a synthetic dataset $\mathcal{S} = \{\tilde{\mathbf{x}}_j\}_{j = 1}^M$ which is significantly smaller ($M \ll N$) such that a model trained from scratch on $\ma…

• The Capability Manifold and ML Scaling Laws

  Syed Ali Raza Zaidi, Maryam Hafeez · 24 de septiembre de 2026

  Existing machine learning (ML) scaling laws relate predictive loss to compute, model parameters, and data. However, as models are increasingly deployed through agentic harnesses, loss alone is insufficient to characterize downstream performance: models with similar loss can exhibit different capabil…

• PhyMo: A Physical-Field Modality for Multimodal AI4Physics

  Henan Sun, Haitao Hu, Jin Liu, Jianfeng Zhang, Lujia Pan, Nuo Chen, Jia Li · 24 de septiembre de 2026

  Multimodal learning is emerging as a powerful paradigm for AI for Physics (AI4Physics), where predicting physical systems requires the joint interpretation of heterogeneous observations, measurements, and domain knowledge. However, existing approaches typically represent physical quantities and gove…

• Exactness at Inference: A Representational Criterion for Out-of-Distribution Generalization

  Filipe Marinho Rocha, In\^es Dutra, V\'itor Santos Costa, Lu\'is Paulo Reis · 22 de septiembre de 2026

  A model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not an approximation fitted to it. Such equivalence is necessary for exactness in and out of distribution, and extrapolation is governed by this exactness…

• Discovering Physical Representation Languages

  Linzhe Zhang, Changming Xu · 22 de septiembre de 2026

  Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or extensive, which sectors are dual, and which distinctions are merely gauge. We introduce physical representation-language discovery, the problem of recov…

• Ananke: Contractive Torus Attractor Networks

  Zhongping Ji · 22 de septiembre de 2026

  We introduce Ananke, a representation-learning framework that scaffolds latent representations onto a structured product-torus prior, and its flagship visual backbone realization, Contractive Torus Attractor Networks (CTAN). By factorizing high-dimensional latent spaces into an orthogonal direct sum…

• Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective

  Chenye Ke, Zirui Liu, Qi Liu, Yan Zhuang, Jintao Zhang, Zhenya Huang, Shijin Wang · 21 de septiembre de 2026

  Detecting pretraining data in large language models is challenging because high likelihood can reflect either training exposure or strong generalization. In the joint space of prediction loss and predictive entropy, a likelihood-only detector uses a horizontal boundary and can mistake predictable no…

• Special Lagrangian cones in Deep Learning

  Tejas Kotwal, Govind Menon · 18 de septiembre de 2026

  We introduce a matrix generalization of the cone of Harvey and Lawson and prove that it is an exact special Lagrangian manifold. We further show that it belongs to a family of exact special Lagrangian manifolds that foliate the balanced manifold arising in deep learning.…

• Surrogate Modeling of 3D Rayleigh-Benard Convection with Equivariant Autoencoders

  Fynn Fromme, Hans Harder, Christine Allen-Blanchette, Sebastian Peitz · 14 de septiembre de 2026

  The use of machine learning for modeling, understanding, and controlling large-scale physics systems is quickly gaining in popularity, with examples ranging from electromagnetism over nuclear fusion reactors and magneto-hydrodynamics to fluid mechanics and climate modeling. These systems - governed …

• Interpreting the predictions of neural network classification based on a Taylor Coefficient Analysis (TCA)

  Markus Klute, Artur Monsch, Lars Sowa, Roger Wolf · 14 de septiembre de 2026

  We introduce a rigid and comprehensive taxonomy and paradigm for characterizing the influence of the input feature space $X$ on the predictions $\hat{y}$ of a neural network (NN) used for event classification, based on a Taylor expansion of $\hat{y}$ in $X$. The complete process of introspection we …

• Physics-Informed Multi-Task Surrogate Model for the Martian Nightside Thermosphere

  Sergey Nikiforov · 10 de septiembre de 2026

  Modeling the Martian nightside thermosphere remains challenging due to sparse in situ sampling and strong coupling among transport, magnetic, and seasonal processes. Purely data-driven models can produce non-physical artifacts, such as density inversions, in poorly sampled altitude regimes. We pre…

• Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization

  Andy Zeyi Liu, Haoran Sun, Lucas Baker, Randall Balestriero, John Sous · 10 de septiembre de 2026

  Joint-Embedding Predictive Architecture (JEPA) world models learn a compact latent representation of the world that supports prediction and planning, but their capability to learn physics and generate physically realistic dynamics remains hitherto untested. In this work, we introduce SemiGroup-JEPA …

• TNFlow: Amortized Posterior Inference for Trans-Neptunian Object Surface Composition

  Agastya Gaur (University of Illinois Urbana-Champaign, SETI Institute), Cristina M. Dalle Ore (Carl Sagan Center, SETI Institute), Alessandra Ricca (NASA Ames Research Center, NASA Ames Research Center) · 7 de septiembre de 2026

  We present TNFlow, a transformer and normalizing flow architecture for inferring the surface composition of Trans-Neptunian Objects (TNOs) from their reflectance spectra. TNFlow is trained on synthetic spectra generated by the Shkuratov radiative transfer model to act as its inverse. TNFlow takes ${…

• A Spectral Phase Admissibility Certificate for Complex Linear Maps

  Snigdha Chandan Khilar · 4 de septiembre de 2026

  The paper imports the Kontsevich Segal Witten criterion from quantum gravity into machine learning to evaluate complex linear maps Standard techniques analyze magnitude or positive definiteness whereas this method exclusively limits the collective phase of a spectrum The researchers create three dis…

• Dandelion: A Spherical Flower for Neural Simulation of Planetary Dynamics

  Till Muser, Giovanni Abati, Ivan Dokmani\'c · 31 de agosto de 2026

  Many dynamical processes unfold on the sphere but the default scientific machine learning architectures are Euclidean. Applying these architectures on a regular lat-lon grid causes problems: Cartesian convolutions become distorted at high latitude; 2D FFTs in Fourier neural operators incorrectly ass…

• Short Horizons and Sparse Concepts: a Mathematical View of the Readout in the J-lens

  Shi-Qi Yan, Kai-Xuan Ding, Chao-Hong Tan, Qian Chen, Wen Wang, Xiangang Li, Zhen-Hua Ling · 27 de agosto de 2026

  The Jacobian lens (J-lens) has been proposed as a way to read verbalizable representations from language models. However, its principle and meaning lack a detailed and theoretical discussion. We provide a mathematical view of this interpretation and of its assumed causal structure. Besides treating …

• PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation

  Sang Won Lee, Hyogu Jeong, Namwoo Kang · 26 de agosto de 2026

  Generative and predictive artificial intelligence models are increasingly used to generate geometry and to predict physical fields and scalar quantities in engineering design and simulation. Yet these models are typically evaluated in isolation, on academic datasets at unconstrained scales, with inc…

• Correcting a learned physical invariant improves world-model rollouts

  Richard Bao · 25 de agosto de 2026

  World models can predict video without learning dynamics that they reliably preserve. We test whether a frozen DreamerV3 trained only on pendulum video learns a scalar that its own latent transition treats as approximately conserved. A label-free search recovers the same energy-like invariant across…

• The Quality of Claude AI-authored Python Tests Is Not Weaker Than Human-authored Tests

  Douglas J. Leith · 18 de agosto de 2026

  We evaluate the quality of Claude AI-written Python tests against human-written Python tests from two established open-source projects Django and Pandas. Hundreds of tests per corpus are scored under one identical protocol. Using one-sided non-inferiority bounds, we find that the tests written by re…