Search Results for author:
Found 7 papers, 2 papers with code
On the Quantification of Image Reconstruction Uncertainty without Training Data
This, in turn, facilitates a probabilistic interpretation of observational data for decision-making.
Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling
In the scope of "AI for Science", solving inverse problems is a longstanding challenge in materials and drug discovery, where the goal is to determine the hidden structures given a set of desirable properties.
Atomic structure generation from reconstructing structural fingerprints
These methods would help identify or, in the case of generative models, even create novel crystal structures of materials with a set of specified functional properties to then be synthesized or isolated in the laboratory.
A Hybrid Gradient Method to Designing Bayesian Experiments for Implicit Models
However, the approach in Kleinegesse et al., 2020 requires a pathwise sampling path to compute the gradient of the MI lower bound with respect to the design variables, and such a pathwise sampling path is usually inaccessible for implicit models.
A Scalable Gradient-Free Method for Bayesian Experimental Design with Implicit Models
However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models.
Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
Unlike the existing studies of DL for TO, our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient.
Robust data-driven approach for predicting the configurational energy of high entropy alloys
In this study, a robust data-driven framework based on Bayesian approaches is proposed and demonstrated on the accurate and efficient prediction of configurational energy of high entropy alloys.
