Title: Learning Specialized Activation Functions for Physics-informed Neural Networks. Abstract: At the heart of network architectures lie the non-linear activation functions, the choice of which affects the model optimization and task performance. In computer vision and natural language processing, the Rectified Linear Unit is widely adopted across different tasks. However, there is no such default choice of activation functions in the context of physics-informed neural networks (PINNs). It is observed that PINNs exhibit high sensitivity to activation functions due to the various characteristics of each physics system, which makes the choice of the suitable activation function for PINNs a critical issue. Existing works usually choose activation functions in an inefficient trial-and-error manner. To address this problem, we propose to search automatically for the optimal activation function when solving different PDEs. This is achieved by learning an adaptive activation function as linear combinations of a set of candidate functions, whose coefficients can be directly optimized by gradient descent. In addition to its efficient optimization, the proposed method enables the discovery of novel activation function and the incorporation with prior knowledge about the PDE system. We can further enhance its search space with adaptive slope. The effectiveness of the proposed adaptive activation function is demonstrated on a series of benchmarks, including the Poisson's equation, Burgers' equation, Allen-Cahn equation, convection equation, Korteweg–de Vries equation and Cahn-Hilliard equation. The performance gain of the proposed method is further interpreted from the neural tangent kernel perspective. Code will be released. 