arXiv:2508.08935v4 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have attracted considerable attention for their ability to integrate partial differential equation priors into deep learning frameworks; however, they often exhibit limited predictive accuracy when applied to complex problems. To address this issue, we propose LNN-PINN, a physics-informed neural network framework that incorporates a liquid residual gating architecture while preserving the original physics modeling and optimization pipeline to improve predictive accuracy. The method introduces a lightweig
The continuous evolution of AI research pushes for more robust and accurate models, especially in integrating physical laws, prompting innovations like LNN-PINN to overcome limitations in complex problem solving.
Improved predictive accuracy in Physics-informed Neural Networks broadens the application of AI in scientific discovery, engineering, and climate modeling, potentially accelerating R&D cycles and optimizing physical systems.
The proposed LNN-PINN framework offers a more reliable method for integrating partial differential equations into deep learning, enhancing the fidelity of AI models in physics-based simulations.
- · Scientific research institutions
- · Engineering firms
- · AI model developers
- · Climate modeling initiatives
Enhanced predictive accuracy in AI models for physical systems through improved PINN architectures.
Accelerated discovery of new materials, more efficient industrial processes, and better environmental predictions.
Reduced reliance on purely empirical methods in science and engineering, leading to faster innovation cycles and lower costs in R&D.
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Read at arXiv cs.AI