Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore an innovative approach to solving partial differential equations using neural networks in this hour-long lecture on Competitive Physics Informed Networks. Delve into the limitations of traditional physics-informed neural networks (PINNs) and discover how the new competitive PINNs (CPINNs) method achieves unprecedented accuracy. Learn about the adversarial training process, where a discriminator and PINN engage in a zero-sum game, leading to solutions with relative errors on par with single-precision accuracy. Examine numerical experiments on a Poisson problem demonstrating CPINNs' superior performance, achieving errors four orders of magnitude smaller than conventional PINNs. Gain insights into the theoretical foundations, implementation challenges, and potential applications of this groundbreaking technique in computational physics and engineering.