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ORCIDNathan M. Urban
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- affiliation: Brookhaven National Laboratory, Upton, NY, USA
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2020 – today
- 2024
[j5]A. N. M. Nafiz Abeer
, Nathan M. Urban, M. Ryan Weil, Francis J. Alexander, Byung-Jun Yoon:
Multi-objective latent space optimization of generative molecular design models. Patterns 5(10): 101042 (2024)- [c9]Sanket R. Jantre, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon:
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks. ICASSP 2024: 5330-5334 - [c8]Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian:
When Uncertainty-Based Active Learning May Fail? ICPR (1) 2024: 84-100 - [c7]A. N. M. Nafiz Abeer, Sanket R. Jantre, Nathan M. Urban, Byung-Jun Yoon:
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design. MLSP 2024: 1-6 - [c6]Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon:
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data. MLSP 2024: 1-6 - [i8]Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon:
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data. CoRR abs/2401.16936 (2024) - [i7]A. N. M. Nafiz Abeer, Sanket R. Jantre, Nathan M. Urban, Byung-Jun Yoon:
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design. CoRR abs/2405.00202 (2024) - [i6]A. N. M. Nafiz Abeer, Sanket R. Jantre, Nathan M. Urban, Byung-Jun Yoon:
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders. CoRR abs/2405.20573 (2024) - [i5]Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian:
Understanding Uncertainty-based Active Learning Under Model Mismatch. CoRR abs/2408.13690 (2024) - [i4]Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon:
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data. CoRR abs/2409.17367 (2024) - 2023
- [c5]Sungduk Yu, Walter M. Hannah, Liran Peng, Jerry Lin, Mohamed Aziz Bhouri, Ritwik Gupta, Björn Lütjens, Justus C. Will, Gunnar Behrens, Julius Busecke, Nora Loose, Charles Stern, Tom Beucler, Bryce E. Harrop, Benjamin R. Hillman, Andrea M. Jenney, Savannah L. Ferretti, Nana Liu, Animashree Anandkumar, Noah D. Brenowitz, Veronika Eyring, Nicholas Geneva, Pierre Gentine, Stephan Mandt, Jaideep Pathak, Akshay Subramaniam, Carl Vondrick, Rose Yu, Laure Zanna, Tian Zheng, Ryan Abernathey, Fiaz Ahmed, David C. Bader, Pierre Baldi, Elizabeth A. Barnes, Christopher S. Bretherton, Peter M. Caldwell, Wayne Chuang, Yilun Han, Yu Huang, Fernando Iglesias-Suarez, Sanket R. Jantre, Karthik Kashinath, Marat Khairoutdinov, Thorsten Kurth, Nicholas J. Lutsko, Po-Lun Ma, Griffin Mooers, J. David Neelin, David A. Randall, Sara Shamekh, Mark Taylor, Nathan M. Urban, Janni Yuval, Guang Zhang, Mike Pritchard:
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. NeurIPS 2023 - [i3]Sanket R. Jantre, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon:
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks. CoRR abs/2309.03061 (2023) - 2022
- [i2]A. N. M. Nafiz Abeer, Nathan M. Urban, M. Ryan Weil, Francis J. Alexander, Byung-Jun Yoon:
Multi-Objective Latent Space Optimization of Generative Molecular Design Models. CoRR abs/2203.00526 (2022) - 2021
- [j4]Pake Melland, Jason Albright, Nathan M. Urban:
Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme. Mach. Learn. Sci. Technol. 2(2): 25015 (2021) - [c4]Divya Banesh, Nishant Panda, Ayan Biswas, Luke Van Roekel, Diane Oyen, Nathan M. Urban, Michael Grosskopf, Jonathan David Wolfe, Earl Lawrence:
Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. IEEE BigData 2021: 3731-3739 - [c3]Soumya Dutta, Natalie Klein, Li Tang, Jonathan David Wolfe, Luke Van Roekel, James Joseph Benedict, Ayan Biswas, Earl Lawrence, Nathan M. Urban:
In Situ Climate Modeling for Analyzing Extreme Weather Events. ISAV@SC 2021: 18-23 - [c2]Michael Grosskopf, Earl Lawrence, Ayan Biswas, Li Tang, Kelin Rumsey, Luke Van Roekel, Nathan M. Urban:
In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes. ISAV@SC 2021: 31-36 - 2020
- [c1]Subhashis Hazarika, Ayan Biswas, Phillip J. Wolfram, Earl Lawrence, Nathan M. Urban:
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data. IEEE VIS (Short Papers) 2020: 41-45 - [i1]Subhashis Hazarika, Ayan Biswas, Phillip J. Wolfram, Earl Lawrence, Nathan M. Urban:
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data. CoRR abs/2008.13306 (2020)
2010 – 2019
- 2019
- [j3]Anthony M. DeGennaro, Nathan M. Urban:
Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation. SIAM J. Sci. Comput. 41(3): A1482-A1499 (2019) - 2013
- [j2]Thomas E. Fricker, Jeremy E. Oakley, Nathan M. Urban:
Multivariate Gaussian Process Emulators With Nonseparable Covariance Structures. Technometrics 55(1): 47-56 (2013) - 2010
- [j1]Nathan M. Urban, Thomas E. Fricker:
A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model. Comput. Geosci. 36(6): 746-755 (2010)
Coauthor Index
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