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A physics-informed graph attention network is proposed as a surrogate for TCAD simulation, enabling faster design space exploration by predicting mesh-node values and incorporating carrier-transport physics.
This paper compares Fourier spectral differentiation and spatial automatic differentiation in periodic physics-informed neural networks, finding that Fourier methods achieve significant training speedups and memory reductions without compromising accuracy.
DeSyR is a decoupled symbolic recovery framework that combines PINN-guided structure search with physics-informed coefficient refinement to accurately recover symbolic solutions for differential equations.
This paper proposes a Physics-Informed Error Field Learning (PIEFL) framework for Physics-Informed Neural Networks, introducing an auxiliary error network to improve solution accuracy in solving partial differential equations under computational constraints.
This study investigates the benefits of frequency decomposition for Physics-Informed Neural Networks (PINNs) by proposing a dual-branch, spectrally-gated architecture (DBSG-PINN). Ablation experiments on 1D PDE benchmarks indicate that frequency decomposition is most effective on spectrally complex problems, reducing error by up to 59.2%.
This study systematically evaluates physics-informed neural network techniques for fluid dynamics, showing that combining periodic activations with causal weighting improves performance on the Navier-Stokes vortex shedding benchmark, while further additions cause degradation due to nonlinear interactions.
The paper introduces a reference-free instrument to detect and discriminate operator misspecification in hybrid PDE-parameter learning from a single fit, demonstrating its effectiveness on a parabolic inverse problem while separating it from parameter unidentifiability.
The paper proposes a physics-informed deep learning approach for lithium-ion battery state-of-health estimation using incomplete discharge curves, achieving high accuracy and enabling real-time degradation trend prognosis.
This paper proposes FALM-PINN, an alternating Levenberg-Marquardt training framework for physics-informed neural networks that uses Fourier-enhanced features to address spectral bias and representation-coefficient coupling, achieving up to two orders of magnitude lower errors on high-frequency and nonlinear PDEs.
This paper proposes SCORE, a self-concordance-inspired quasi-Newton method for training physics-informed neural networks (PINNs). It uses a decrement-coupled shifted secant geometry to improve final accuracy on nonlinear PDE benchmarks without requiring Hessian computations.
This paper introduces GeoID-PINN, a physics-informed neural network for regional epidemic inference that incorporates geographic coupling via a spatial source-composition matrix. It demonstrates that accurate trajectory forecasts do not guarantee correct recovery of regional dependence structures, using simulations and COVID-19 data from Louisiana counties.
The paper introduces a hybrid quantum-classical framework enhancing Quantum Physics-Informed Neural Networks (QPINNs) with adaptive collocation point sampling and loss-aware attention for solving differential equations, achieving significant accuracy improvements in fluid dynamics and reaction-diffusion benchmarks.
This paper introduces feature interaction modules based on factorization machines into physics-informed neural networks and neural operators (FM-PINN, FM-Operator, FM-DeepONet) to better capture spatio-temporal variable couplings for solving parameterized PDEs, showing accuracy gains particularly on shock-dominated equations.
EvoPINN is an agentic framework that reformulates PINN development as an execution-grounded algorithm discovery problem, using an LLM agent to propose programmatic modifications. It autonomously discovers PDE-specialized learning algorithms, including a novel architecture called SLRC-PINN, which outperforms baselines across diverse PDE regimes.
This paper traces step-by-step how PyTorch's automatic differentiation engine computes gradients for Physics-Informed Neural Network training, including the two levels of differentiation needed for physics residuals and parameter gradients, using a simple MLP and ODE example.
This paper presents a hybrid analytical-PINN model for simulating geothermal heat exchangers in heterogeneous subsurface, removing singularities and enabling efficient neural network training via an analytical correction approach.
This paper evaluates five model families for macroeconomic forecasting, finding that less-constrained models like ARIMA and Neural ODE outperform structurally-prioritized models like PINNs, highlighting that structural priors can act as misregularizers when mismatched.
This paper presents a physics-informed neural network (PINN) framework for modeling transient elastodynamic wave propagation in bimaterial systems, using a steel-aluminum specimen from a Split Hopkinson Pressure Bar. The PINN accurately predicts wave transmission and reflection, validated against high-fidelity finite-element simulations, and serves as a continuous surrogate model for elastodynamic analysis.
This work-in-progress paper proposes embedding a differentiable physics model into the PPO actor loss function to penalize anticipated safety violations in reinforcement learning, evaluated on a simulated 1-DoF helicopter system. The physics-informed soft regularizations reduce constraint violations while maintaining reliable target tracking.
This paper introduces DigiTurbine, a synthetic reliability-aware Physics-Informed Neural Network (PINN) benchmark for offshore wind turbine monopile support-structure monitoring, combining forward and inverse PINN with Bayesian prior-informed identification and FORM-based reliability screening.