"Efficient Technique for Solving Partial Differential Equations in Diverse Applications"
Researchers have proposed a new method called "physics-enhanced deep surrogates" (PEDS) for developing data-driven surrogate models to efficiently solve complex physical systems governed by partial differential equations (PDEs) in fields such as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate models. This method combines a low-fidelity physics simulator with a neural network generator, resulting in surrogates that are up to three times more accurate than traditional neural networks and require significantly less training data. The technique offers accuracy, speed, data efficiency, and physical insights into the process, making it a promising tool for a wide range of applications in engineering and beyond.