#physics

18 curated events

papersTODAY 04:00 UTC

Study Assesses Chern-Simons Context Reservoir as Computing Substrate

A new preprint examines whether a reservoir built on Chern-Simons gauge theory can serve as a practical computational substrate. The authors model the reservoir state as a density fluctuation on a two-dimensional surface and test whether letting the gauge connection evolve adds capability beyond simpler designs. The work is exploratory and frames the question around feasibility and memory mechanisms rather than reporting a deployed system.

reservoir-computing/arxiv/physics/research

papersTODAY 04:00 UTC

arXiv paper models bow control on a measured bowed-string model

A new arXiv preprint presents a finite-difference bowed-string simulation that resolves Stribeck friction implicitly, alongside a diagnostic for playing regimes and Schelleng bow-force limits computed for four strings. It also revisits a minimum-bow-force law and compares a recurrent learned controller against a supervision ceiling that constrains how much imitation can help.

arxiv/physics/control/simulation

papersTODAY 04:00 UTC

PhysMent benchmark evaluates LLM physics reasoning through interactive experiments

Researchers introduced PhysMent, a benchmark designed to test how well large language models reason about physical systems by running experiments rather than answering static questions. The work argues that strong scores on existing science benchmarks do not show whether models can actively probe the physical world. The abstract notes that this ability remains poorly understood.

benchmarks/llm-reasoning/physics/arxiv

papersTODAY 04:00 UTC

Autoencoder Method Targets Quasinormal Mode Parameter Estimation in Ringdown Signals

A new arXiv preprint describes using an autoencoder to estimate the parameters of ringdown gravitational waves, which are modeled as combinations of quasinormal modes carrying information about the remnant Kerr black hole. The work focuses on reliably extracting multiple quasinormal modes, a task that is difficult with conventional fitting methods. The paper appears in the cross-listed machine learning category.

gravitational-waves/autoencoders/physics/research

papersTODAY 04:00 UTC

Neural Modal Decomposition Derives Architectural Priors from Observables

A new arXiv preprint introduces neural modal decomposition, an approach that infers architectural priors for multi-port linear time-invariant systems by observing their behavior rather than relying on hand-specified designs. The authors note that RF cavities, photonic components, and superconducting quantum circuits, though physically distinct, can be described by a shared mathematical framework, which the method exploits. The work positions itself at the intersection of machine learning architecture design and engineering system modeling.

arxiv/research/machine-learning/physics

papersTODAY 04:00 UTC

Quenched Ensemble Sampling Tackles Phase-Transition Bottlenecks in Sampling

A new arXiv preprint introduces Quenched Ensemble Sampling, a method aimed at the difficulty of drawing samples from physical energy functions near phase transitions, where the density of states shifts sharply and many existing samplers slow or stall. The work builds on nested sampling, a particle-based approach that traverses such landscapes. The abstract indicates the technique is designed to keep sampling efficient in these abrupt regime changes.

sampling/nested-sampling/physics/research

papersTODAY 04:00 UTC

Deep Autoencoder Estimates Intrinsic Dimensionality of FPUT Trajectories

The paper applies a deep autoencoder to estimate the intrinsic dimensionality of high-dimensional trajectories from the Fermi-Pasta-Ulam-Tsingou beta model with 32 oscillators. The dataset spans roughly 4 million data points, and the authors take a nonlinear approach to characterize the underlying low-dimensional structure of the dynamics.

autoencoders/physics/dimensionality-reduction/research

papersTODAY 04:00 UTC

Symplectic Neural Networks Target Non-Separable Hamiltonian Systems

A revised arXiv paper proposes a symplectic neural network approach for learning non-separable Hamiltonians directly from noisy state observations. Hamiltonian Neural Networks embed physical priors by learning a system's energy function, which can improve generalization and reduce data needs compared with standard models. The work focuses on extending this to systems whose Hamiltonians cannot be split into kinetic and potential parts.

research/physics/neural-networks/arxiv

papersTODAY 04:00 UTC

Study finds video models store correct physics but fail to apply it

A new arXiv paper asks whether video generators that produce physically implausible motion never learned the correct dynamics or merely fail to use what they learned. The authors introduce a notion of causal writability to probe this, and report that the correct motion is still represented inside the model and can be made to steer generation. The finding points to a gap between internal knowledge and how it is used, rather than a simple absence of physical understanding.

video-models/physics/interpretability/generative-models

papersTODAY 04:00 UTC

arXiv paper applies neural networks to real-space charge density, generalization

A new arXiv preprint examines using neural networks to represent ground-state electron charge density in real space. The work is motivated by the Hohenberg-Kohn theorem, which holds that ground-state density encodes all ground-state information about a many-electron system. The authors also study how well such learned models generalize.

arxiv/neural-networks/physics/research

papersTODAY 04:00 UTC

Probing Video Foundation Models for Intuitive Physics Representations

A study examines whether pretrained video foundation models hold intuitive-physics information in their frozen representations. Using frozen-feature probing on the IntPhys2 benchmark, the authors compare how this information differs across model families, layers, and probe types. The work is a cross-listed arXiv replacement submission in cs.AI and cs.LG.

video-models/probing/physics/research

papersTODAY 04:00 UTC

arXiv Paper Addresses Machine Learning of Metastable Dynamics

A new preprint on arXiv examines how machine learning can be used to identify and model metastable behavior in physical systems. Metastability describes systems that linger in quasi-stable states and then shift abruptly under rare perturbations, a pattern seen across many areas of physics. The work targets the challenge of detecting and representing these transitions computationally.

research/arxiv/physics/dynamical-systems

papersTODAY 04:00 UTC

Backward SDE Approach Targets Physics Consistency in Diffusion Models

A new arXiv preprint proposes using backward stochastic differential equations to enforce physics and measurement consistency when score-based diffusion models are applied to inverse problems. The authors argue that existing methods, such as heuristic guidance, periodic projections, or task-specific conditional training, are less principled. The work positions the approach as a general way to keep pretrained diffusion priors aligned with physical constraints.

diffusion/research/physics/generative-models

papersSEP 10 04:00 UTC

Researchers Use Reinforcement Learning to Hunt for Physics Beyond the Standard Model

A new research paper explores applying reinforcement learning to searches for new physics in particle physics, focusing on anomalies where low-energy measurements deviate from Standard Model predictions. The work targets one of the field's most important open problems: identifying evidence of physics beyond the Standard Model.

reinforcement-learning/physics/research/anomaly-detection

papersSEP 10 04:00 UTC

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

A new arXiv preprint proposes Semigroup-JEPA, a joint-embedding predictive architecture augmented with a latent dynamics consistency objective. The approach is designed to help world models capture physical behavior so that predicted dynamics remain plausible in unseen scenarios without retraining. The paper was posted to arXiv's cs.AI and cs.LG listings.

world-models/jepa/physics/zero-shot

papersSEP 10 04:00 UTC

Self-Evolving AI Agent Designs Interpretable White-Box Fluid Control

Researchers report a self-evolving scientific agent that designs control strategies for dynamic fluid systems without relying on neural networks. The resulting white-box controllers are explicit and physically reasoned, making autonomous control decisions easier to interpret and diagnose.

agents/research/interpretability/physics

papersSEP 10 04:00 UTC

New Paper Addresses Mode Coverage Gaps in Normalizing Flow Boltzmann Generators

A new arXiv study examines why normalizing flow Boltzmann generators trained with forward KL divergence can miss portions of a target distribution when the available training samples are biased or incomplete. The authors propose using variation in the log-ratio of importance weights as a signal to detect when a flow's samples fail to cover target modes. The approach aims to improve the reliability of flow-based samplers used in statistical physics and molecular simulation.

generative-models/sampling/physics/machine-learning

papersSEP 12 04:00 UTC

PINN Framework Infers Perpendicular Heat Conductivity in Stellarator Scrape-Off Layer

Researchers present an inverse physics-informed neural network that estimates how the scrape-off layer's perpendicular heat conductivity varies with plasma density and temperature in stellarator devices. The approach embeds physical constraints into the learning process rather than relying solely on labeled data, allowing the conductivity function to be recovered from available measurements. This is an arXiv preprint on fusion plasma modeling and has not yet been peer reviewed.

physics/neural-networks/fusion/scientific-ml