Deep Learning for Physics Simulation

Numerical simulation of physical systems has become an increasingly important scientific tool supporting various research fields. Despite its remarkable success, simulating intricate physical systems typically requires advanced domain-specific knowledge, meticulous implementation, and enormous computational resources. With the surge of deep learning in the last decade, there has been a growing interest in the machine-learning and graphics communities to address these limitations of numerical simulation with deep learning. This course provides a gentle introduction to this topic for audiences interested in exploring this trend but with little to modest machine-learning or physics-simulation backgrounds. We begin with a brief overview of the numerical simulation framework on which we ground our discussion of deep-learning methods. Next, the course provides a possible classification of several hybrid simulation strategies based on the roles of learning and physics insights incorporated. We then review the implications of such deep-learning strategies and discuss some practical considerations in combining deep learning and physics simulation. Finally, we briefly mention several advanced machine-learning techniques for further exploration. The full course information can be found in https://people.iiis.tsinghua.edu.cn/~taodu/dl4sim/.