Physic Constrained

Physics-constrained deep learning integrates physical laws and principles directly into neural network architectures to improve the accuracy, robustness, and generalizability of models for various scientific and engineering problems. Current research focuses on developing and refining physics-informed neural networks (PINNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), often employing techniques like soft and hard constraints, multi-fidelity data, and various optimization strategies to enhance performance. This approach is proving valuable across diverse fields, enabling more accurate predictions and efficient solutions for complex systems where traditional methods struggle, particularly in scenarios with limited or noisy data.