Knowledge-driven learning, optimization, and experimental design under uncertainty for materials discovery
Significant acceleration of the future discovery of novel functional materials requires a
fundamental shift from the current materials discovery practice, which is heavily dependent …
fundamental shift from the current materials discovery practice, which is heavily dependent …
Multi-objective latent space optimization of generative molecular design models
Molecular design based on generative models, such as variational autoencoders (VAEs),
has become increasingly popular in recent years due to its efficiency for exploring high …
has become increasingly popular in recent years due to its efficiency for exploring high …
A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows
The capability to replicate the predictions by machine learning (ML) or artificial intelligence
(AI) models and the results in scientific workflows that incorporate such ML/AI predictions is …
(AI) models and the results in scientific workflows that incorporate such ML/AI predictions is …
Quantifying the multi-objective cost of uncertainty
Various real-world applications involve modeling complex systems with immense
uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the …
uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the …
Robust importance sampling for error estimation in the context of optimal Bayesian transfer learning
Classification has been a major task for building intelligent systems because it enables
decision-making under uncertainty. Classifier design aims at building models from training …
decision-making under uncertainty. Classifier design aims at building models from training …
Accelerating optimal experimental design for robust synchronization of uncertain Kuramoto oscillator model using machine learning
Recent advances in objective-based uncertaintyquantification (objective-UQ) have shown
that such a goal-driven approach for quantifying model uncertainty is extremely usefulin real …
that such a goal-driven approach for quantifying model uncertainty is extremely usefulin real …
Identifying Bayesian optimal experiments for uncertain biochemical pathway models
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that
include drug mechanisms of action. These models are useful for studying predictive …
include drug mechanisms of action. These models are useful for studying predictive …
Neural message-passing for objective-based uncertainty quantification and optimal experimental design
Various real-world scientific applications involve the mathematical modeling of complex
uncertain systems with numerous unknown parameters. Accurate parameter estimation is …
uncertain systems with numerous unknown parameters. Accurate parameter estimation is …
Optimal Decision Making for Accelerating Scientific Discovery
HM Woo - 2022 - oaktrust.library.tamu.edu
Scientific discovery is the process of finding answers to scientific inquiries. Scientific
discovery forms the basis of scientific/engineering applications as it serves as an operational …
discovery forms the basis of scientific/engineering applications as it serves as an operational …
[引用][C] AI for Optimal Experimental Design and Decision-Making
Artificial Intelligence for Science: AI for Optimal Experimental Design and Decision-Making
Page 1 c 2023 World Scientific Publishing Company https://doi.org/10.1142/9789811265679 …
Page 1 c 2023 World Scientific Publishing Company https://doi.org/10.1142/9789811265679 …