Time Dependent

Time-dependent problems, particularly solving the time-dependent Schrödinger equation, are a central focus in computational physics and chemistry, aiming to accurately model the evolution of quantum systems. Current research emphasizes developing efficient numerical methods, leveraging deep neural networks (like FermiNets and physics-informed neural networks) and novel algorithms (including those based on normalizing flows and Hamilton-Jacobi PDEs) to overcome the computational challenges associated with high-dimensional systems. These advancements improve the accuracy and speed of simulations for various applications, ranging from materials science to quantum control, offering significant potential for accelerating scientific discovery and technological innovation.