physics-informed

KernelOnet proposes an interpretable neural operator framework that explicitly incorporates kernel functions in place of trunk networks, offering data-driven, physics-informed, and hybrid kernel strategies for PDE solving with higher accuracy and fewer parameters than DeepONet.

The paper introduces the Transformer-Mamba for Flow Field (TM4FF) framework, a physics-constrained operator learning model that enhances accuracy and robustness in computational fluid dynamics simulations through innovations like Residual Wavelet Mamba and physics-informed loss.

The paper proposes a Physics Informed Recurrent Neural Network (PIRNN) that predicts unobservable physical variables to improve time series forecasting in physical processes, demonstrated through groundwater level predictions.

Drift Field Net (DFN) is a deep neural network that predicts ocean surface flow fields from satellite observations, using a two-stage training strategy to improve particle trajectory accuracy. It reduces positioning errors by 20 km in 7-day forecasts compared to operational models, with further gains from Lagrangian fine-tuning.

The paper introduces Physics-Informed Conformal Prediction (PI-CP), a framework that embeds PDE residuals into conformal prediction to provide distribution-free, spatially adaptive uncertainty quantification for neural operators like the Fourier Neural Operator (FNO).

The paper introduces Fundamental Dynamical Units (FDUs) as composable primitives for physics-informed structural inference in networked dynamical systems, using neural ODEs to recover interaction structures from perturbation time-series data.

GenONet is a novel deep learning architecture that uses Deep Operator Networks within a Generative Adversarial Network framework for high-resolution precipitation nowcasting, producing sharp and physically consistent forecasts.

The paper proposes a self-augmented diffusion guidance method to incorporate physical laws into diffusion models, reducing deviations from true dynamics and enabling faster generation.

This paper introduces a physics-integrated operator learning framework using a feed-forward Gaussian splatting representation to directly incorporate PDE operators, reducing errors in long-horizon autoregressive predictions for spatiotemporal systems.

A novel AI model using a Fourier neural operator variant enables density functional theory to scale nearly linearly with system size, allowing efficient simulations of large quantum systems like a magnesium dislocation with 80k electrons on a single GPU.

PhysCaP is a physics-informed code-generation agent that actively explores objects to infer hidden physical properties for efficient robotic manipulation.

PIKFNO is a new interpretable neural operator framework that integrates physics-informed kernel functions from governing equations to enhance predictive accuracy and interpretability with limited training data.

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.