Physic Based Model

Physics-based models, foundational in many scientific and engineering disciplines, are increasingly being augmented or replaced by data-driven approaches, particularly deep learning, to improve efficiency and accuracy. Current research focuses on hybrid models that combine the strengths of both paradigms, leveraging neural networks (including recurrent, convolutional, and ordinary differential equation-based architectures) to learn complex relationships while incorporating physical constraints or principles. This integration aims to overcome limitations of purely physics-based or data-driven models, leading to more robust, accurate, and computationally efficient predictions across diverse applications, from weather forecasting and fluid dynamics to robotics and materials science.