Nathan M. Urban

According to our database, Nathan M. Urban authored at least 18 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Multi-objective latent space optimization of generative molecular design models.

A. N. M. Nafiz Abeer, Nathan M. Urban, M. Ryan Weil, Francis J. Alexander, Byung-Jun Yoon

Patterns, 2024

Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy.

Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban, Byung-Jun Yoon

CoRR, 2024

Understanding Uncertainty-based Active Learning Under Model Mismatch.

Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian

CoRR, 2024

Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders.

A. N. M. Nafiz Abeer, Sanket R. Jantre, Nathan M. Urban, Byung-Jun Yoon

CoRR, 2024

Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data.

Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

CoRR, 2024

Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data.

Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

Proceedings of the 34th IEEE International Workshop on Machine Learning for Signal Processing, 2024

Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design.

A. N. M. Nafiz Abeer, Sanket R. Jantre, Nathan M. Urban, Byung-Jun Yoon

Proceedings of the 34th IEEE International Workshop on Machine Learning for Signal Processing, 2024

When Uncertainty-Based Active Learning May Fail?

Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou, Nathan M. Urban, Byung-Jun Yoon, Xiaoning Qian

Proceedings of the Pattern Recognition - 27th International Conference, 2024

Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks.

Sanket R. Jantre, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme.

Pake Melland, Jason Albright, Nathan M. Urban

Mach. Learn. Sci. Technol., 2021

In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes.

Michael Grosskopf, Earl Lawrence, Ayan Biswas, Li Tang, Kelin Rumsey, Luke Van Roekel, Nathan M. Urban

Proceedings of the ISAV@SC 21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, 2021

In Situ Climate Modeling for Analyzing Extreme Weather Events.

Soumya Dutta, Natalie Klein, Li Tang, Jonathan David Wolfe, Luke Van Roekel, James Joseph Benedict, Ayan Biswas, Earl Lawrence, Nathan M. Urban

Proceedings of the ISAV@SC 21: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, 2021

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks.

Divya Banesh, Nishant Panda, Ayan Biswas, Luke Van Roekel, Diane Oyen, Nathan M. Urban, Michael Grosskopf, Jonathan David Wolfe, Earl Lawrence

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data.

Subhashis Hazarika, Ayan Biswas, Phillip J. Wolfram, Earl Lawrence, Nathan M. Urban

Proceedings of the 31st IEEE Visualization Conference, 2020

2019

Scalable Extended Dynamic Mode Decomposition Using Random Kernel Approximation.

Anthony M. DeGennaro, Nathan M. Urban

SIAM J. Sci. Comput., 2019

2013

Multivariate Gaussian Process Emulators With Nonseparable Covariance Structures.

Thomas E. Fricker, Jeremy E. Oakley, Nathan M. Urban

Technometrics, 2013

2010

A comparison of Latin hypercube and grid ensemble designs for the multivariate emulation of an Earth system model.

Nathan M. Urban, Thomas E. Fricker

Comput. Geosci., 2010