Search Results for author:
Found 3 papers, 0 papers with code
Efficient Inverse Design Optimization through Multi-fidelity Simulations, Machine Learning, and Search Space Reduction Strategies
Notably, this method is adeptly adaptable across any inverse design application, facilitating a harmonious synergy between a representative low-fidelity machine learning model, and high-fidelity simulation, and can be seamlessly applied across any variety of population-based optimization algorithms.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Adaptive Gaussian process surrogates for Bayesian inference
We adaptively construct training designs by maximizing the expected improvement in fit of the Gaussian process model to the noisy observational data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
