Personal information

Verified email domains

Activities

Education and qualifications (1)

California Institute of Technology: Pasadena, CA, US

2012-09-01 to 2017-06-15 | PhD (Applied and Computational Mathematics)
Education
Source: Self-asserted source
Tommie Catanach

Works (50 of 65)

Items per page:
Page 1 of 2

Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo

arXiv preprint arXiv:2403.18072
2024 | Journal article
Contributors: Zhong, Shijie; Shen, Wanggang; Catanach, Tommie; Huan, Xun
Source: Self-asserted source
Tommie Catanach

Logical activation functions for training arbitrary probabilistic boolean operations

Information Sciences
2024 | Journal article
Contributors: Duersch, Jed A; Catanach, Tommie A; Das, Niladri
Source: Self-asserted source
Tommie Catanach

A physics-based machine learning approach to quantum device characterization

APS March Meeting Abstracts
2023 | Conference paper
Contributors: Hothem, Daniel; Young, Kevin; Proctor, Timothy; Catanach, Tommie
Source: Self-asserted source
Tommie Catanach

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

2023 | Journal article
Contributors: Carter, Jonathan; Feddema, John; Kothe, Doug; Neely, Rob; Pruet, Jason; Stevens, Rick; Balaprakash, Prasanna; Beckman, Pete; Foster, Ian; Iskra, Kamil et al.
Source: Self-asserted source
Tommie Catanach

Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter

Computational Mechanics
2023 | Journal article
Contributors: Villarreal, Ruben; Vlassis, Nikolaos N; Phan, Nhon N; Catanach, Tommie A; Jones, Reese E; Trask, Nathaniel A; Kramer, Sharlotte LB; Sun, WaiChing
Source: Self-asserted source
Tommie Catanach

Learning a quantum computer's capability using convolutional neural networks

arXiv preprint arXiv:2304.10650
2023 | Journal article
Contributors: Hothem, Daniel; Young, Kevin; Catanach, Tommie; Proctor, Timothy
Source: Self-asserted source
Tommie Catanach

Metrics for Bayesian Optimal Experiment Design Under Model Misspecification

2023 62nd IEEE Conference on Decision and Control (CDC)
2023 | Conference paper
Contributors: Catanach, Tommie A; Das, Niladri
Source: Self-asserted source
Tommie Catanach

Variational Kalman Filtering with Hinf-Based Correction v. 1.0

2023 | Report
Contributors: Catanach, Thomas; Das, Niladri
Source: Self-asserted source
Tommie Catanach

A Study of Bias-Variance in Variational Inferencing Using Delta Method.

2022 | Report
Contributors: Das, Niladri; Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Adaptive High-Arity Logical Activation Functions.

2022 | Report
Contributors: Duersch, Jed; Catanach, Thomas; Das, Niladri
Source: Self-asserted source
Tommie Catanach

Adaptive n-ary Activation Functions for Probabilistic Boolean Logic

arXiv preprint arXiv:2203.08977
2022 | Journal article
Contributors: Duersch, Jed A; Catanach, Thomas A; Das, Niladri
Source: Self-asserted source
Tommie Catanach

Analysis of Bias-variance Trade-off in Estimators for Variational Inferencing.

2022 | Report
Contributors: Das, Niladri; Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Assessing the Limits of Predictive Uncertainty in Seismic Event Discrimination Using Bayesian Neural Networks.

2022 | Report
Contributors: Garcia, Jorge; Linville, Lisa; Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

How to benchmark a 100-qubit quantum computer using fewer than 100 circuits.

2022 | Report
Contributors: Proctor, Timothy; Rudinger, Kenneth; Seritan, Stefan; Stefan, Daniel; Hothem, Daniel; Hines, Jordan; Catanach, Thomas; Blume-Kohout, Robin; Young, Kevin
Source: Self-asserted source
Tommie Catanach

Learning the Capabilities of Quantum Computers.

2022 | Report
Contributors: Proctor, Timothy; Hothem, Daniel; Hines, Jordan; Seritan, Stefan; Sarovar, Mohan; Catanach, Thomas; Young, Kevin
Source: Self-asserted source
Tommie Catanach

Posterior Predictive Variational Inference for Uncertainty Quantification in Machine Learning.

2022 | Report
Contributors: Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Predicting circuit success rates with artificial neural networks

APS March Meeting Abstracts
2022 | Conference paper
Contributors: Hothem, Daniel; Young, Kevin; Catanach, Thomas; Proctor, Timothy
Source: Self-asserted source
Tommie Catanach

Predicting the success rates of quantum circuits with artificial neural networks.

2022 | Report
Contributors: Hothem, Daniel; Catanach, Thomas; Young, Kevin; Proctor, Timothy
Source: Self-asserted source
Tommie Catanach

Quantifying the Impact of Simulation Frequency Fidelity on Waveform-Based Bayesian Inference for Seismic Monitoring Using Bayesian Experimental Design.

2022 | Report
Contributors: Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Reinforcement Learning for Material Calibration Via Kalman Filter Estimation.

2022 | Report
Contributors: Villarreal Jr, Ruben; VLASSIS, NIKOLAOS; Catanach, Thomas; Jones, Reese; Trask, Nathaniel; Kramer, Sharlotte Lorraine Bolyard; Sun, WaiChing
Source: Self-asserted source
Tommie Catanach

Reinforcement Learning in Material Science Research.

2022 | Report
Contributors: Das, Niladri; Villarreal, Ruben; Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Variational Kalman Filtering with H∞-Based Correction for Robust Bayesian Learning in High Dimensions

2022 IEEE 61st Conference on Decision and Control (CDC)
2022 | Conference paper
Contributors: Das, Niladri; Duersch, Jed A; Catanach, Tommie A
Source: Self-asserted source
Tommie Catanach

Analysis and Optimization of Seismo-Acoustic Monitoring Networks with Bayesian Optimal Experimental Design

2021 | Report
Contributors: Catanach, Thomas A; Monogue, Kevin
Source: Self-asserted source
Tommie Catanach

CIS Project 22359, Final Technical Report. Discretized Posterior Approximation in High Dimensions

2021 | Report
Contributors: Duersch, Jed; Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Expected information gain estimates and Bayesian optimal experimental design.

2021 | Report
Contributors: Alsup, Terrence; Catanach, Thomas
Source: Self-asserted source
Tommie Catanach

Generalized Transitional Markov Chain Monte Carlo Sampling Technique for Bayesian Inversion

arXiv preprint arXiv:2112.02180
2021 | Journal article
Contributors: Lu, Han; Khalil, Mohammad; Catanach, Thomas; Chen, Jiefu; Wu, Xuqing; Fu, Xin; Safta, Cosmin; Huang, Yueqin
Source: Self-asserted source
Tommie Catanach

Local to regional scale simulation of small events with 2D finite differences.

2021 | Report
Contributors: Porritt, Robert; Linville, Lisa; Conley, Andrea; Catanach, Thomas; Tibi, Rigobert; Merchant, Bion; Downey, Nathan; Young, Christopher
Source: Self-asserted source
Tommie Catanach

Modeling Capabilities of Waveform Feature-Based Bayesian Inference for Seismic Monitoring

AGU Fall Meeting Abstracts
2021 | Conference paper
Contributors: Catanach, Thomas; Porritt, Robert; Young, Christopher
Source: Self-asserted source
Tommie Catanach

Parsimonious inference

arXiv preprint arXiv:2103.02165
2021 | Journal article
Contributors: Duersch, Jed A; Catanach, Thomas A
Source: Self-asserted source
Tommie Catanach

Parsimonious Inference Foundations for Trustworthy-by-Design Machine Learning.

2021 | Report
Contributors: Duersch, Jed Alma; Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

A Bayesian Perspective on Machine Learning and UQ.

2020 | Report
Contributors: Catanach, Thomas Anthony; Duersch, Jed Alma
Source: Self-asserted source
Tommie Catanach

A Generalized Theory of Information to Improve Algorithmic Learning and Predictions.

2020 | Report
Contributors: Duersch, Jed Alma; Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Bayesian Inference and Sequential Tempered Markov Chain Monte Carlo for Synthetic Biological Systems.

2020 | Report
Contributors: Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Bayesian inference of stochastic reaction networks using multifidelity sequential tempered Markov chain Monte Carlo

International journal for uncertainty quantification
2020 | Journal article
Contributors: Catanach, Thomas A; Vo, Huy D; Munsky, Brian
Source: Self-asserted source
Tommie Catanach

Bayesian Optimal Experimental Design for Seismic Monitoring.

2020 | Report
Contributors: Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Characterization of Partially Observed Epidemics-Application to COVID-19

2020 | Report
Contributors: Safta, Cosmin; Ray, Jaideep; Acquesta, Erin; Catanach, Thomas Anthony; Chowdhary, Kamaljit Singh; Debusschere, Bert; Galvan, Edgar; Geraci, Gianluca; Khalil, Mohammad; Portone, Teresa
Source: Self-asserted source
Tommie Catanach

Generalizing information to the evolution of rational belief

Entropy
2020 | Journal article
Contributors: Duersch, Jed A; Catanach, Thomas A
Source: Self-asserted source
Tommie Catanach

Modeling Failure of Electrical Transformers due to Effects of a HEMP Event

2020 | Report
Contributors: Hansen, Clifford W; Catanach, Thomas Anthony; Glover, Austin Michael; Huerta, Jose Gabriel; Stuart, Zach; Guttromson, Ross
Source: Self-asserted source
Tommie Catanach

Multifidelity Sequential Tempered Markov Chain Monte Carlo for Bayesian Inference.

2020 | Report
Contributors: Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Parsimonious Rational Belief.

2020 | Report
Contributors: Duersch, Jed Alma; Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Seismic Monitoring with Feature-based Bayesian Inference.

2020 | Report
Contributors: Catanach, Thomas Anthony; Downey, Nathan John; Young, Christopher J
Source: Self-asserted source
Tommie Catanach

Feature-based Bayesian Inference and Sequential Tempered Markov Chain Monte Carlo for Seismic Event Monitoring

AGU Fall Meeting Abstracts
2019 | Conference paper
Contributors: Catanach, Thomas Anthony; Downey, Nathan; Young, Chris
Source: Self-asserted source
Tommie Catanach

Feature-Based Bayesian Inference for Seismic Event Monitoring.

2019 | Report
Contributors: Catanach, Thomas Anthony; Downey, Nathan John; Young, Christopher J
Source: Self-asserted source
Tommie Catanach

Predictive Uncertainty Quantification: Reinterpreting Machine Learning as Machine Prediction from the Bayesian Perspective.

2019 | Report
Contributors: Catanach, Thomas Anthony; Duersch, Jed Alma
Source: Self-asserted source
Tommie Catanach

Seismic Monitoring with Feature-based Bayesian Inference and Sequential Tempered MCMC.

2019 | Report
Contributors: Catanach, Thomas Anthony; Downey, Nathan John; Young, Christopher J
Source: Self-asserted source
Tommie Catanach

Sequential Tempered Markov Chain Monte Carlo: Accelerating Bayesian Inference Model Selection and Uncertainty Quantification.

2019 | Report
Contributors: Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Accelerating Sequential Tempered MCMC for Fast Bayesian Inference and Uncertainty Quantification.

2018 | Report
Contributors: Catanach, Thomas Anthony
Source: Self-asserted source
Tommie Catanach

Bayesian updating and uncertainty quantification using sequential tempered mcmc with the rank-one modified metropolis algorithm

arXiv preprint arXiv:1804.08738
2018 | Journal article
Contributors: Catanach, Thomas A; Beck, James L
Source: Self-asserted source
Tommie Catanach

Context dependence of biological circuits

bioRxiv
2018 | Journal article
Contributors: Catanach, Thomas A; McCardell, Reed; Baetica, Ania-Ariadna; Murray, Richard M
Source: Self-asserted source
Tommie Catanach

Efficient Generalizable Deep Learning

2018 | Report
Contributors: Catanach, Thomas Anthony; Duersch, Jed Alma
Source: Self-asserted source
Tommie Catanach
Items per page:
Page 1 of 2