Title: Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks. Abstract: Physics-informed neural network (PINN) algorithms have shown promising results in solving a wide range of problems involving partial differential equations (PDEs). However, even the simplest PDEs, often fail to converge to desirable solutions when the target function contains high-frequency modes, due to a phenomenon known as spectral bias. In the present work, we exploit neural tangent kernels (NTKs) to investigate the training dynamics of PINNs evolving under stochastic gradient descent with momentum (SGDM). This demonstrates SGDM significantly reduces the effect of spectral bias. We have also examined why training a model via the Adam optimizer can accelerate the convergence while reducing the spectral bias. Moreover, our numerical experiments have confirmed that wide-enough networks using SGDM or Adam still converge to desirable solutions, even in the presence of high-frequency features.