Forward Model

Forward models are mathematical or computational representations of systems that predict an output given an input, crucial for solving inverse problems where the goal is to infer unknown parameters from observed data. Current research emphasizes developing more efficient and robust forward models, often employing neural networks (e.g., recurrent, convolutional, or physics-embedding networks) and advanced algorithms like Bayesian inference, to handle complex systems, noisy data, and uncertainties in model parameters. This work is significant because accurate forward models are essential for diverse applications, ranging from medical imaging and geophysical modeling to robotics and materials science, enabling improved data analysis, parameter estimation, and ultimately, better decision-making in these fields.