physics-informed machine learning

topic9 events

papersTODAY 04:00 UTC

Paper tests physics-based assumption in RL simulators and world models

A new arXiv preprint examines the widely held assumption that learned dynamics models which conform to underlying physics produce more accurate predictions. The authors recover exact polynomial invariants from trajectories and canonicalise them as a method for diagnosing faults in reinforcement learning simulators and world models. The work frames these invariants as a diagnostic tool for checking whether a model's learned dynamics actually respect physical structure.

reinforcement-learning/world-models/simulators/arxiv

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

Multifidelity TDNN and Physics-Informed Residual Learning for Railway Bogie Prediction

A revised arXiv preprint proposes a hybrid approach for predicting railway bogie responses, combining a time-delay neural network with physics-informed residual learning across simulations of differing fidelity. The method targets operating conditions that are impractical to test exhaustively, using agreement with representative measurements as validation evidence. It sits within ongoing work on surrogate modeling for engineering simulation.

railways/simulation/physics-informed/neural-networks

papersSEP 12 04:00 UTC

CryptoL Framework Targets Scale Imbalance in Cryptocurrency Forecasting

A new arXiv paper introduces CryptoL, a unified framework for forecasting multivariate cryptocurrency time series. The work addresses cross-asset scale differences, non-stationary market dynamics, and dependencies among open, high, low, and close price variables. It proposes physics-informed constraints to mitigate these issues in financial prediction.

crypto/forecasting/time-series/arxiv

papersSEP 11 04:00 UTC

Graybox machine learning approach applied to Bayesian quantum sensing

A new arXiv preprint describes a graybox machine learning method for Bayesian quantum sensing. The approach combines physics-informed modeling with data-driven components to improve how quantum sensors estimate parameters. It aims to address practical performance limits that keep quantum sensors from reaching their theoretical advantages in fields such as materials science and healthcare.

quantum/machine-learning/research/sensing

papersSEP 10 04:00 UTC

Researchers build physics-informed surrogate model for Mars' nightside thermosphere

A new arXiv paper introduces a multi-task surrogate model that combines physical constraints with machine learning to simulate the Martian nightside thermosphere. The problem is difficult because direct measurements are sparse and transport, magnetic, and seasonal effects interact strongly, so purely data-driven approaches can produce unphysical outputs such as reversed density trends. Embedding physics into the training process aims to keep the model's predictions consistent with known atmospheric behavior.

space/physics-informed/machine-learning/research

papersSEP 10 04:00 UTC

Kolmogorov-Arnold Networks Applied to Refine Nuclear Mass Models

A new arXiv preprint uses Kolmogorov-Arnold Networks, an interpretable neural architecture, to improve theoretical models that predict the masses of atomic nuclei. The authors address the difficulty of learning from limited and highly complex nuclear datasets, aiming to blend physics-based theory with data-driven corrections.

kolmogorov-arnold-networks/interpretability/nuclear-physics/scientific-ml

papersSEP 10 04:00 UTC

WaveGraphNet couples inverse and forward graph learning for guided-wave damage localization

A research paper introduces WaveGraphNet, a graph-based approach that localizes damage in composite plates from sparse networks of piezoelectric sensors using guided waves. By jointly training inverse and forward models under physics-consistency constraints, the method aims to overcome the weak supervision that limits standard approaches when only a few sensor measurements are available.

graph-neural-networks/physics-informed/structural-monitoring/sparse-sensing

papersSEP 10 04:00 UTC

PRIME-SVR: Physics-informed slice-to-volume reconstruction for fetal brain T2 mapping

Researchers have introduced PRIME-SVR, a physics-informed implicit reconstruction method that turns motion-corrupted 2D fetal MRI slices into high-resolution 3D brain volumes with quantitative T2 mapping. The approach builds on standard slice-to-volume reconstruction by adding multi-echo signal modeling, targeting improved quantitative imaging of the developing fetal brain.

medical-imaging/mri/fetal-health/deep-learning