11 papers
Transferable Physics-Informed Representations via Closed-Form Head Adaptation
Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu +3
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physica…
FFV-PINN: A Fast Physics-Informed Neural Network with Simplified Finite Volume Discretization and Residual Correction
Chang Wei, Yuchen Fan, Jian Cheng Wong +3
Physics-informed neural networks (PINNs) have emerged as a major research focus. However, today's PINNs encounter several limitations. Firstly, during the construction of the loss…
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…
Out-of-Distribution Generalization for Neural Physics Solvers
Zhao Wei, Chin Chun Ooi, Jian Cheng Wong +3
Neural physics solvers are increasingly used in scientific discovery, given their potential for rapid in silico insights into physical, materials, or biological systems and their l…