#physics-informed

11 curated events

papersTODAY 04:00 UTC

HGTO: Graph-Based Physics-Informed Formulation for Structural Topology Optimization

A new arXiv preprint introduces HGTO, a unified graph-based, physics-informed framework for density-based structural topology optimization. The approach reframes the usual nested loop of material updates, structural analysis, and sensitivity computation, drawing on neural density parameterization and dual-field physics-informed methods that require no labeled data. The abstract positions the work as a data-free alternative to conventional optimization pipelines.

research/physics-informed/optimization/graph-neural-networks

papersTODAY 04:00 UTC

Physics-Regularized Symbolic Modeling of 3nm FinFETs via Universal Differential Equations

A new arXiv preprint presents PRISM-UDE, a method that blends universal differential equations with physics-based regularization to build compact models of 3nm FinFET transistors. Hand-derived compact models struggle to capture transport behavior at advanced nodes, while purely data-driven neural surrogates can sacrifice physical consistency. The approach aims to yield interpretable, equation-based device models suitable for circuit simulation.

semiconductors/physics-informed/symbolic-regression/arxiv

papersTODAY 04:00 UTC

Linearized PINN Uses Pretrained Nonlinear Layers for Solving Differential Equations

Researchers introduce lPINN, a reduced-order neural basis approach for both forward and inverse differential equation problems. In an offline phase, the method derives operator-compatible continuous neural basis functions from an ensemble, then solves equations in a lower-dimensional space. The work aims to combine physics-informed modeling with the efficiency of pretrained linearized representations.

research/physics-informed/neural-networks/differential-equations

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

papersTODAY 04:00 UTC

ACR-PINN: Layer-wise Adaptation and Gradient Conflict Resolution for PINNs

Researchers propose ACR-PINN, a physics-informed neural network framework that pairs layer-wise dynamic adaptation of coordinate representations with a method for resolving conflicts among gradients coming from heterogeneous physical constraints. The work frames architecture and optimization as a joint design problem, aiming to improve training when competing constraints pull the model in different directions.

physics-informed/optimization/neural-networks/research

papersTODAY 04:00 UTC

Physics-Constrained Neural Surrogate Models Domain Growth Under Conserved Kinetics

A revised arXiv preprint presents a neural surrogate model that incorporates physical constraints to predict how domains grow in systems governed by conserved kinetics. Such systems are typically described by nonlinear partial differential equations, which are costly to solve, and learned surrogates aim to offer faster alternatives. The work targets physics, chemistry, and biology applications where accurate spatiotemporal prediction matters.

research/neural-surrogates/physics-informed/simulation

papersTODAY 04:00 UTC

PINN framework models blood flow dynamics in abdominal aortic aneurysms

Researchers built a three-dimensional physics-informed neural network to simulate pulsatile blood flow in the human aorta, focusing on abdominal aortic aneurysm haemodynamics. The approach embeds physical laws into the training process, allowing time-resolved simulation without conventional mesh-based solvers. The work is a preprint posted to arXiv.

research/physics-informed/healthcare/simulation

papersSEP 11 04:00 UTC

Physics-constrained neural networks speed up RCWA surrogate modeling of periodic structures

A new arXiv paper presents a physics-constrained neural network that predicts rigorous coupled-wave analysis outputs directly as Jones matrices for lossless layered periodic structures. The approach builds on energy conservation to constrain the model, aiming to replace or accelerate conventional simulations that are computationally expensive. The work falls under machine learning for scientific computing and photonics design.

physics-informed/neural-networks/surrogate-modeling/photonics

papersSEP 10 04:00 UTC

Physics-Guided Machine Learning Extrapolation Framework Validated on Diffusion Benchmark

A new arXiv paper introduces a physics-guided machine learning framework designed to make reliable predictions outside the limited operating ranges in which engineering models are typically trained. The authors argue that extrapolation, rather than interpolation, is the central challenge for applied ML, and they validate their approach using a classical transient diffusion problem as a benchmark.

physics-informed/extrapolation/machine-learning/research

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

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