physics-informed

PhysAttNet is a physics-informed attention framework that augments lightweight CNN forecasters with domain-guided regularization to improve accuracy and generalization in industrial and astrophysical time series forecasting.

This preprint proposes PI-HNO, a physics-informed hybrid neural operator for transient magnetization prediction in power magnetics, achieving low B-H energy consistency errors with only 4,777 trainable parameters per material model.

This paper evaluates Kolmogorov–Arnold Networks (KANs) versus MLPs as residual branches in hard-constrained recurrent physics-informed networks on an embedded RISC-V platform, finding KANs run slower, consume more energy, and are less dependable under INT8 quantization.

This paper introduces PiDDM, a physics-informed differentiable degradation modeling framework that embeds battery degradation kinetics into neural network training to improve lithium-ion battery state-of-health prediction accuracy and physical consistency across diverse cycling protocols.

This paper introduces FatigueCV, a physics-informed deep learning framework that predicts steel fatigue life from optical micrographs in under 65ms, using a CNN with uncertainty estimation. Validation on synthetic micrographs shows strong performance (R²=0.93), though real-world validation is noted as future work.

LithoFormer is a Seq2Seq transformer framework for stratigraphic inference from well logs, using a PatchTST backbone with rotary positional embeddings and a multi-task head to jointly predict geological zonation and boundaries, achieving a 90% reduction in boundary error and eliminating stratigraphic order violations.

OrbitAll is a molecular foundation model that uses physics-grounded orbital features and SE(3)-equivariant GNNs to predict molecular properties, outperforming larger models like UMA with 35x less training data and 50x smaller model size.

Introduces latent PDE mapping, a physics-informed learning technique that enables efficient geometric generalization with sparse training data by pulling back PDE residuals to a latent geometry. Demonstrates significant error reduction on cardiac electrophysiology PDE benchmarks using only 15 training samples.

This paper presents a novel framework for zero-shot Digital Twins that integrates real-time visual perception with a geometry-agnostic, physics-informed Graph Neural Network. The approach uses a Thermodynamics-Informed GNN to enforce energy conservation and entropy production, achieving physically accurate simulations on unseen geometries without retraining.

This paper proposes ChemHyperMag, a physics-informed magnetic hypergraph learning method for multitask ADMET prediction that uses functional group hypergraphs and a Hermitian magnetic Laplacian to capture asymmetric interactions and directional signals, improving prediction accuracy with fewer labeled samples.

This paper proposes a Neural Controlled Differential Equation (Neural CDE) framework for learning continuous-time surrogate models of grid-forming inverters for EMT simulation, incorporating dual slow/fast pathways and physics-inspired regularization to capture multi-time-scale dynamics.

This paper introduces Lorentz Encoding (LE), a physics-informed framework that uses implicit neural representations and physical constraints to reconstruct high-resolution CEST MRI from sparsely sampled data, achieving superior performance over existing methods.

Introduces UniWind, a physics-informed machine learning model for day-ahead wind power forecasting that combines physical prior estimation with latent state encoding to handle operational states like shutdowns and curtailment, achieving robust performance across multiple real-world datasets.

A blog post explaining Hamiltonian Neural Networks through differential geometry, using a simple mass-spring system to demonstrate how imposing conservation laws via network architecture can lead to more efficient learning. The author builds up mathematical tools like symplectic manifolds and Poisson brackets from basic calculus.

This paper presents a physics-informed conditional normalizing flow model for angles-only orbit determination in the cislunar environment, enabling flexible posterior representation and providing warm starts for classical algorithms.

LithoDreamer is the first physics-informed World Model framework for computational lithography, modeling the multi-stage lithography process as a decision-driven system. It achieves state-of-the-art performance in forward evolution and inverse planning for semiconductor manufacturing.

TRIDENT is a novel multi-agent reinforcement learning framework that breaks the coupling between hybrid discrete-continuous actions, hard safety constraints, and physics-governed dynamics, achieving provably safe coordination with a convergence guarantee to a constrained Nash equilibrium and significant reductions in training-time violations.

This paper presents a Hybrid NARX-LLM framework for predicting Greenland iceberg discharge, using a Physics-Informed Prompt method to guide an LLM for residual correction, improving accuracy over traditional NARX models.

Physics-conforming Latent Twins is a framework for learning latent surrogate solution operators that enforce physical principles such as conservation laws and dissipative inequalities by design, using a constraint-transfer approach and structure-preserving latent dynamics.

SwiftCTS is a physics-informed surrogate framework that uses gradient-boosted ensembles and few-shot calibration to rapidly predict and Pareto-optimize clock tree metrics (power, wirelength, timing skew) across unseen designs, achieving high accuracy with minimal training data.