Advancements and Future Directions in Loss Function Designs for Physics-Informed Neural Networks: A Comprehensive Review
This review synthesizes recent advances in loss function designs for Physics-Informed Neural Networks (PINNs), a transformative approach to solving partial differential equations (PDEs) by embedding physical laws into deep learning frameworks. We begin by exploring the foundational role of loss functions in deep neural networks and their adaptation in PINNs to enforce PDE residuals, boundary conditions, and data constraints. The article systematically categorizes core loss components and early optimization strategies, including collocation point selection, which underpin effective PINN training. A comprehensive survey examines advanced loss function designs, including adaptive and self-adaptive weighting strategies, alternative formulations such as weak forms, energy-based, and gradient-enhanced methods, as well as uncertainty quantification and training enhancements like meta-learning, transfer learning, loss landscape engineering, regularization techniques, and optimization algorithms. We evaluated their impacts on convergence speed, predictive accuracy, and computational efficiency in applications such as fluid dynamics, solid mechanics, wave propagation, and geotechnical modeling. Challenges such as gradient imbalances, multi-scale dynamics, and high-dimensional scaling are critically analyzed, with emerging solutions like automated loss balancing, physics-aware regularization, and innovative architectures proposed to enhance robustness. A comparative analysis highlights performance trade-offs, while future directions emphasize dynamic optimization, hierarchical multi-fidelity losses, and scalable uncertainty quantification. This structured synthesis, drawing on developments up to 2025, equips researchers with insights to refine PINN methodologies, bridging data-driven and physics-based paradigms for transformative scientific computing.