6 papers
Adaptive Beam Selection for Efficient Scanning Probe Tomography
San Dinh, Zichao Wendy Di, Matt Menickelly
In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs, acquisition time and the rad…
Discrepancy Modeling with Intermediate Variables: A New Framework for Robust Gaussian Process Calibration
Henry Shaowu Yuchi, Michael Grosskopf, Aman Sharma +4
Gaussian processes are widely used for surrogate modeling in computer experiments, which often produce numerous intermediate variables that are not explicitly used in standard cali…
Birkhoff interpolation models for optimization with some available derivatives
Jeffrey Larson, Matt Menickelly, Evan Toler
We consider interpolation-based derivative-free optimization in settings where only some derivatives are available. Such situations arise in scientific computing applications invol…
Importance Sampling in Expensive Finite-Sum Optimization via Contextual Bandit Methods
Matt Menickelly
In computational science workflows, it is often the case that 1) objective functions for optimization involve multiple simulation outputs, and 2) those simulations can be performed…
HFBTHO-AD: Differentiation of a nuclear energy density functional code
Laurent Hascoët, Matt Menickelly, Sri Hari Krishna Narayanan +3
The HFBTHO code implements a nuclear energy density functional solver to model the structure of atomic nuclei. HFBTHO has previously been used to calibrate energy functionals and p…
A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly +1
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS lever…