Deep Learning for Physics Simulation
Abstract: 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/.
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