LiL-Q applies quasilinearization to nonlinear PDEs and solves each resulting linear problem by convex least-squares collocation on Linear-in-Learnables trial spaces, achieving fast convergence and high accuracy on multiple benchmarks.
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
2 Pith papers cite this work. Polarity classification is still indexing.
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Digi Turbine is a synthetic PINN benchmark integrating Euler-Bernoulli beam theory with Winkler foundation, Bayesian inverse identification, and FORM screening for OWT monopile monitoring, validated on synthetic data with analytical ground truth.
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A Convex Quasilinearization Method for Solving Nonlinear PDEs with Physics-Informed Neural Networks
LiL-Q applies quasilinearization to nonlinear PDEs and solves each resulting linear problem by convex least-squares collocation on Linear-in-Learnables trial spaces, achieving fast convergence and high accuracy on multiple benchmarks.
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A Synthetic Reliability-Aware PINN Benchmark for Offshore Wind Turbine Support-Structure Monitoring with Bayesian Inverse Identification
Digi Turbine is a synthetic PINN benchmark integrating Euler-Bernoulli beam theory with Winkler foundation, Bayesian inverse identification, and FORM screening for OWT monopile monitoring, validated on synthetic data with analytical ground truth.