31 citations · 57 across the 18 of their papers we have counts for
19 papers
Machine learning assisted Bayesian calibration of an accelerator digital twin from orbit response data
Christopher Kelly, Kevin A. Brown, Georg H. Hoffstaetter +2
Digital twins of particle accelerators are used to plan and control operations and to design data collection campaigns. Accurate modeling typically requires knowledge of quantities…
Flexible and Stable Dynamics Discovery with Onsager's Variational Principle
Anthony Gruber, Ritoban Roy-Chowdhury, Irina Tezaur +1
Variational principles specify the dynamics of a physical system via the extremization of associated functional data. Onsager's variational principle (OVP), which characterizes dis…
Data-efficient surrogate modeling of spectral functions using Gaussian processes: An application to the --- model
Sanket Jantre, Nathan M. Urban, Weiguo Yin +1
Spectral functions encode key many-body information but are costly to compute with high fidelity. Machine-learning surrogates have emerged as a powerful alternative, yet many appro…
Data-Augmented Few-Shot Neural Emulator for Computer-Model System Identification
Sanket Jantre, Deepak Akhare, Zhiyuan Wang +2
Partial differential equations (PDEs) underpin the modeling of many natural and engineered systems. It can be convenient to express such models as neural PDEs rather than using tra…
Robust, Online, and Adaptive Decentralized Gaussian Processes
Fernando Llorente, Daniel Waxman, Sanket Jantre +2
Gaussian processes (GPs) offer a flexible, uncertainty-aware framework for modeling complex signals, but scale cubically with data, assume static targets, and are brittle to outlie…
Cost-effective Reduced-Order Modeling via Bayesian Active Learning
Amir Hossein Rahmati, Nathan M. Urban, Byung-Jun Yoon +1
Machine Learning surrogates have been developed to accelerate solving systems dynamics of complex processes in different science and engineering applications. To faithfully capture…
C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang +4
Low-Rank Adaptation (LoRA) offers a cost-effective solution for fine-tuning large language models (LLMs), but it often produces overconfident predictions in data-scarce few-shot se…
Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Sanket Jantre, Tianle Wang, Gilchan Park +5
Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex conditions such as neurodegenerative d…
Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson +2
The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured mo…
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data
Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban +2
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance…
Understanding Uncertainty-based Active Learning Under Model Mismatch
Amir Hossein Rahmati, Mingzhou Fan, Ruida Zhou +3
Instead of randomly acquiring training data points, Uncertainty-based Active Learning (UAL) operates by querying the label(s) of pivotal samples from an unlabeled pool selected bas…
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for…
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frame…
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 +2
World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Win…
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon +2
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeuti…
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Sanket Jantre, Nathan M. Urban, Xiaoning Qian +1
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the…
ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation
Sungduk Yu, Zeyuan Hu, Akshay Subramaniam +44
Modern climate projections lack adequate spatial and temporal resolution due to computational constraints, leading to inaccuracies in representing critical processes like thunderst…
Sequential Bayesian Neural Subnetwork Ensembles
Sanket Jantre, Shrijita Bhattacharya, Nathan M. Urban +4
Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. H…
Multi-Objective Latent Space Optimization of Generative Molecular Design Models
A N M Nafiz Abeer, Nathan Urban, M Ryan Weil +2
Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimen…