Tag: Physics-Informed Neural Networks
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Efficient Geometric Adaptation for Physics-Informed Neural Nets
Discover a lightweight geometric adaptation framework that boosts training speed, stability, and accuracy of Physics-Informed Neural Networks (PINNs).
Physics-Guided CNN-BiLSTM Beats Attention in Solar Forecasting
Discover how physics-guided CNN-BiLSTM models outperform attention mechanisms in accurate, efficient solar irradiance forecasting for renewable energy.
Self-supervised PINNs for Stellar Structure Equations
Discover how self-supervised physics-informed neural networks solve stellar structure equations efficiently, outperforming traditional methods like MESA.
Curvature-Aware Optimization Boosts Physics-Informed Neural Nets
Discover curvature-aware optimization techniques that enhance accuracy and speed in physics-informed neural networks solving complex differential equations...
General Explicit Network: Deep Learning for PDE Solutions
Discover the General Explicit Network (GEN), a robust deep learning model improving partial differential equation solving with enhanced accuracy and extens...