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.

通过对生物学中的鲍德温效应的生物学透镜的研究，本文首次研究了学习能够同时概括整个物理任务系列的物理知情神经网络（Physics-informed neural networks）的潜力。借鉴能够快速学习、预测和快速适应环境的早成品种的神经发育，我们构想了一种对物理学的高效学习产生强偏差的预连结强度的物理知情神经网络。为此，我们将进化选择压力（通过在一系列任务上的熟练程度进行指导）与终身学习（以在更小的一部分任务子集上专精）相结合，以生成展示出快速和符合物理规律的预测能力的物理知情神经网络，可以适应一系列充满挑战的实例问题。与通过梯度下降元学习的物理知情神经网络相比，鲍德温方法在预测准确性上提供了数量级的提升，并大大降低了计算成本。本文标志着物理知情神经网络作为通用物理求解器的元学习迈向了一个新的台阶。