Multi Fidelity Surrogate

Multi-fidelity surrogate modeling combines data from multiple sources of varying accuracy and computational cost to create efficient predictive models. Current research focuses on developing advanced data fusion techniques and leveraging neural network architectures, such as neural processes and LSTMs, to improve scalability and accuracy, particularly for high-dimensional problems. This approach significantly reduces the computational burden of complex simulations across diverse fields, from engineering design optimization to drug discovery and climate modeling, enabling more efficient exploration of parameter spaces and improved decision-making. The resulting cost savings and enhanced predictive capabilities are transforming scientific research and practical applications.