LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks
Researchers have developed LNN-PINN, a new framework designed to enhance the accuracy of physics-informed neural networks (PINNs). This framework integrates a liquid residual gating architecture into the hidden layers of PINNs without altering the core physics modeling or optimization processes. Testing across four benchmark problems demonstrated that LNN-PINN consistently achieved lower RMSE and MAE compared to standard PINNs under identical training conditions. The architecture also proved adaptable and stable across various problem complexities, offering a concise yet effective method for improving predictive capabilities in scientific and engineering applications. AI
IMPACT Enhances predictive accuracy for scientific and engineering problems by refining PINN architectures.