activity
20132026
most citedAccelerating MCMC with active subspaces

63 citations · 308 across the 48 of their papers we have counts for

collaborators

50 papers

cs.CE2026

Dimension Bridging for 3D RANS with Neural Network Accelerated Gaussian Functional Regression

Wesley Lao, Thomas Scott, Tan Bui-Thanh +2

In many computational science and engineering problems, repeatedly solving fully resolved physics-based models to design for a quantity of interest (QoI) can quickly become intract…

cs.LG2026

An optimal control approach for neural network architecture adaptation with a posteriori error estimation

C G Krishnanunni, Thomas Scott, Tan Bui-Thanh

This work presents a novel approach for adapting neural network architecture along the depth based on a posteriori error estimation. By formulating neural network training as a con…

math.ST2026

Laplace-Fisher Gate Identities for Optimal Matrix-Gated Blended Score Estimation

Alois Duston, Tan Bui-Thanh

Sampling from an unnormalized target density by reversing an Ornstein-Uhlenbeck diffusion requires the score of each noise-perturbed marginal law. Two exact identities are availabl…

math.NA2026

Second-order consistency for learning chaotic dynamics via randomized Jacobian matching

Shinhoo Kang, Hai V. Nguyen, Tan Bui-Thanh

Short-horizon accuracy does not ensure that a learned chaotic system has correct long-time dynamics. Trajectory (zeroth-order) matching constrains vector-field values, and Jacobian…

math.OC2026

Rendezvous Planning from Sparse Observations of Optimally Controlled Targets

Thomas A. Scott, Lukas Taus, Yen-Hsi Richard Tsai +2

We develop a probabilistic framework for \emph{rendezvous planning}: given sparse, noisy observations of a fast-moving target, plan rendezvous spatiotemporal coordinates for a set…

cs.LG2026

Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations

Jamie Mahowald, Tan Bui-Thanh

We investigate the generalization capabilities of In-Context Operator Networks (ICONs), a new class of operator networks that build on the principles of in-context learning, for hi…