Generalizable Neural Physics Solvers by Baldwinian Evolution
Jian Cheng Wong Chin Chun Ooi Abhishek Gupta Pao-Hsiung Chiu Joshua Shao Zheng Low My Ha Dao Yew-Soon Ong
Physics-informed neural networks (PINNs) are at the forefront of scientific
machine learning, making possible the creation of machine intelligence that is
cognizant of physical laws and able to accurately simulate them. In this paper,
the potential of discovering PINNs that generalize over an entire family of
physics tasks is studied, for the first time, through a biological lens of the
Baldwin effect. Drawing inspiration from the neurodevelopment of precocial
species that have evolved to learn, predict and react quickly to their
environment, we envision PINNs that are pre-wired with connection strengths
inducing strong biases towards efficient learning of physics. To this end,
evolutionary selection pressure (guided by proficiency over a family of tasks)
is coupled with lifetime learning (to specialize on a smaller subset of those
tasks) to produce PINNs that demonstrate fast and physics-compliant prediction
capabilities across a range of empirically challenging problem instances. The
Baldwinian approach achieves an order of magnitude improvement in prediction
accuracy at a fraction of the computation cost compared to state-of-the-art
results with PINNs meta-learned by gradient descent. This paper marks a leap
forward in the meta-learning of PINNs as generalizable physics solvers.