This paper explores the application of the deep Ritz method (DRM) to learn complex fracture processes in phase-field modeling of brittle fracture. The authors investigate the challenges of using neural networks (NNs) to approximate the energy landscape and the ability of optimization methods to reach the correct energy minimum. They discuss the design and training of NNs that can accurately and efficiently capture all relevant fracture phenomena. The developed method is applied to benchmark problems, showing qualitative and quantitative agreement with finite element (FEA) solutions. The robustness of the approach is tested using NNs with different initializations. The phase-field model of brittle fracture is based on a variational framework that includes the displacement field and the phase field. The energy functional consists of elastic and damage components, with the phase field representing the damage state. The model accounts for crack nucleation, propagation, kinking, branching, and coalescence. The energy functional is non-convex, posing challenges in numerical computation, and the resolution of small length scales is computationally expensive. The authors propose the use of physics-informed deep learning, specifically the deep Ritz method, to learn the solution of phase-field fracture problems. The DRM directly minimizes the energy functional instead of the PDE residual, making it particularly useful for non-convex problems. The method involves constructing an NN to approximate the solution fields and training it to minimize the energy functional. The NN is designed to handle both the displacement and phase fields, with careful attention to the choice of activation functions, gradient computation, and weight regularization. The authors demonstrate the effectiveness of the DRM in learning various fracture phenomena, including crack nucleation, propagation, kinking, branching, and coalescence. They compare the results with FEA solutions, showing close agreement. The method is also tested for robustness by training NNs with different initializations. The computational cost of the DRM is higher than FEA, but the approach is robust and can be used to solve parametric phase-field fracture problems. The study highlights the potential of physics-informed deep learning in modeling complex fracture processes within the phase-field framework.This paper explores the application of the deep Ritz method (DRM) to learn complex fracture processes in phase-field modeling of brittle fracture. The authors investigate the challenges of using neural networks (NNs) to approximate the energy landscape and the ability of optimization methods to reach the correct energy minimum. They discuss the design and training of NNs that can accurately and efficiently capture all relevant fracture phenomena. The developed method is applied to benchmark problems, showing qualitative and quantitative agreement with finite element (FEA) solutions. The robustness of the approach is tested using NNs with different initializations. The phase-field model of brittle fracture is based on a variational framework that includes the displacement field and the phase field. The energy functional consists of elastic and damage components, with the phase field representing the damage state. The model accounts for crack nucleation, propagation, kinking, branching, and coalescence. The energy functional is non-convex, posing challenges in numerical computation, and the resolution of small length scales is computationally expensive. The authors propose the use of physics-informed deep learning, specifically the deep Ritz method, to learn the solution of phase-field fracture problems. The DRM directly minimizes the energy functional instead of the PDE residual, making it particularly useful for non-convex problems. The method involves constructing an NN to approximate the solution fields and training it to minimize the energy functional. The NN is designed to handle both the displacement and phase fields, with careful attention to the choice of activation functions, gradient computation, and weight regularization. The authors demonstrate the effectiveness of the DRM in learning various fracture phenomena, including crack nucleation, propagation, kinking, branching, and coalescence. They compare the results with FEA solutions, showing close agreement. The method is also tested for robustness by training NNs with different initializations. The computational cost of the DRM is higher than FEA, but the approach is robust and can be used to solve parametric phase-field fracture problems. The study highlights the potential of physics-informed deep learning in modeling complex fracture processes within the phase-field framework.