1.2k citations · 1.4k across the 8 of their papers we have counts for
9 papers
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery
Saaketh Desai, Sadhvikas Addamane, Jeffrey Y. Tsao +4
Advances in robotic control and sensing have propelled the rise of automated scientific laboratories capable of high-throughput experiments. However, automated scientific laborator…
Solving High-Dimensional Inverse Problems with Auxiliary Uncertainty via Operator Learning with Limited Data
Joseph Hart, Mamikon Gulian, Indu Manickam +1
In complex large-scale systems such as climate, important effects are caused by a combination of confounding processes that are not fully observable. The identification of sources…
Gaussian Process Regression constrained by Boundary Value Problems
Mamikon Gulian, Ari Frankel, Laura Swiler
We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value p…
A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
Laura Swiler, Mamikon Gulian, Ari Frankel +2
Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a broader effort in scientific machine learning, many recen…
An active learning high-throughput microstructure calibration framework for solving inverse structure-process problems in materials informatics
Anh Tran, John A. Mitchell, Laura P. Swiler +1
Determining a process-structure-property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in t…
Tensor Basis Gaussian Process Models of Hyperelastic Materials
Ari Frankel, Reese Jones, Laura Swiler
In this work, we develop Gaussian process regression (GPR) models of hyperelastic material behavior. First, we consider the direct approach of modeling the components of the Cauchy…
A Spectral Analysis Method for Automated Generation of Quantum-Accurate Interatomic Potentials
Aidan P. Thompson, Laura P. Swiler, Christian R. Trott +2
We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machine-learni…
Spoke-Darts for High-Dimensional Blue-Noise Sampling
Scott A. Mitchell, Mohamed S. Ebeida, Muhammad A. Awad +6
Blue noise sampling has proved useful for many graphics applications, but remains underexplored in high-dimensional spaces due to the difficulty of generating distributions and pro…
Sensitivity analysis of a computational model of the IKK-NF-κB-IκBα-A20 signal transduction network
Jaewook Joo, Steve Plimpton, Shawn Martin +2
The NF-κB signaling network plays an important role in many different compartments of the immune system during immune activation. Using a computational model of the NF-κB signaling…