Parameter Inference

Parameter inference aims to determine the values of unknown parameters within a model based on observed data, a crucial task across diverse scientific fields. Current research emphasizes developing robust and efficient algorithms, including Bayesian methods, neural networks (like Physics-Informed Neural Networks and diffusion models), and evolutionary strategies, often tailored to specific model types (e.g., partial differential equations, agent-based models) and data characteristics (e.g., high-dimensionality, non-stationarity). These advancements improve the accuracy and speed of parameter estimation, enabling more reliable model calibration and predictions in applications ranging from cosmology and climate modeling to battery diagnostics and robotics. The development of efficient surrogates for computationally expensive models is a particularly active area, facilitating high-throughput analyses.