Physics-Enhanced Neural Networks Outperform Traditional Solvers
Feb 10, 2024
Researchers at the Georgia Institute of Technology have introduced physics-enhanced deep surrogate (PEDS) models, a novel approach that combines neural networks and physics simulators to efficiently solve complex physics equations. These models, proven to be up to three times more accurate than traditional neural networks, have promising applications in engineering simulations, offering a significant reduction in required training data. The approach aims to leverage expert knowledge in physics to enhance the learning capabilities of neural networks, providing a more efficient solution to complex mathematical problems in various scientific and engineering fields.
