Inverse Modeling

Inverse modeling aims to determine the underlying parameters or physical properties of a system based on observed outputs, effectively reversing a forward model. Current research heavily utilizes deep learning architectures, such as neural networks (including physics-informed and convolutional variations), mixture density networks, and graph networks, often combined with traditional methods or probabilistic frameworks like Bayesian approaches and ensemble filters, to improve accuracy and efficiency. This field is crucial for diverse applications, ranging from material design and geophysical imaging to hydrological modeling and robotic control, enabling more accurate predictions, efficient simulations, and improved understanding of complex systems.