physics-informed

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.

This paper argues that generative AI for semiconductor manufacturing must enforce hard physical constraints by construction, not via post-hoc filtering, and surveys architectural approaches like physics-informed diffusion and neural-operator priors to achieve physics fidelity.

Proposes a Hamiltonian Transformer, a physics-informed attention mechanism that enforces norm-preserving value dynamics for RF transmitter fingerprinting, achieving 99.12% accuracy in same-day conditions and 61.64% with 150 transmitters, outperforming CNN and Transformer baselines.

This paper presents a comprehensive mathematical framework for sequential surrogate modeling of three-phase black-oil reservoir dynamics using Fourier Neural Operators (FNO) and physics-informed variants (PINO), applied to the Norne benchmark reservoir. Theoretical contributions include functional-analytic formulation, covariate shift analysis, physics-constrained spectral stability, and truncated backpropagation gradient analysis.

Fourier neural operators (FNOs) achieve extrapolation success in modeling periodically driven quantum systems, capturing temporal correlations in frequency space for physically faithful dynamics beyond training data.

This paper develops a PAC-Bayesian framework for physics-informed machine learning, providing high-probability generalization guarantees for unbounded losses. It proposes a multi-task perspective that jointly handles data fidelity, PDE residuals, and boundary conditions, and introduces a self-bounding learning algorithm.

EMMA is a physics-informed multimodal framework that recovers dynamical parameters from raw video, audio, and image data using a Liquid Time-Constant network and physics-constrained loss, outperforming existing baselines across diverse benchmarks.

This paper proposes Physics-Informed Multi-Scale Mamba (PIMSM), a state-space architecture that aligns model memory with physical timescales to improve robustness under distribution shift in scientific time series, demonstrating improvements on fMRI and weather forecasting tasks.

A comprehensive survey reviewing recent advances in using artificial intelligence to solve inverse partial differential equation (PDE) problems, covering inverse problems, inverse design, and control problems, with applications across scientific and industrial domains.