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.

翻译：物理信息神经网络（PINNs）处于科学机器学习的前沿，使得创建具备物理定律认知能力并能精确模拟物理定律的机器智能成为可能。本文首次通过鲍德温效应的生物学视角，研究了发现可泛化至整个物理任务族的PINNs的潜力。受早熟物种神经发育（这些物种已进化出快速学习、预测和响应环境的能力）的启发，我们构想了一种PINNs，其连接强度被预先设定，从而诱导出对高效物理学习具有强烈偏置的倾向。为此，进化选择压力（由对整个任务族的熟练程度引导）与生命周期学习（用于专门处理这些任务中的较小子集）相结合，以生成在一系列具有实证挑战性的问题实例中展现出快速且符合物理定律的预测能力的PINNs。与通过梯度下降元学习的PINNs的最新成果相比，鲍德温方法在计算成本仅为前者一小部分的情况下，将预测精度提升了一个数量级。本文标志着作为可泛化物理求解器的PINNs元学习领域的一次飞跃。