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Bridging Computational Fluid Dynamics Algorithm and Physics-Informed Learning: SIMPLE-PINN for Incompressible Navier-Stokes Equations
Chang Wei, Yuchen Fan, Chin Chun Ooi +3
Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations (PDEs) by directly embedding them into the loss function. Despite their notab…
Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
Pao-Hsiung Chiu, Jian Cheng Wong, Chin Chun Ooi +3
Physics-informed neural networks (PINNs) have emerged as a promising mesh-free paradigm for solving partial differential equations, yet adoption in science and engineering is limit…
PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes
Karkulali Pugalenthi, Jian Cheng Wong, Qizheng Yang +4
Accurate, real-time, yet non-destructive estimation of internal states in lithium-ion batteries is critical for predicting degradation, optimizing usage strategies, and extending o…