226 citations · 729 across the 62 of their papers we have counts for
35 papers · 1 filter
Latent Diffusion Models for Structural Component Design
Ethan Herron, Jaydeep Rade, Anushrut Jignasu +4
Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper…
SpecXAI -- Spectral interpretability of Deep Learning Models
Stefan Druc, Peter Wooldridge, Adarsh Krishnamurthy +2
Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models. These models, however,…
Neural PDE Solvers for Irregular Domains
Biswajit Khara, Ethan Herron, Zhanhong Jiang +8
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention. However, the large majority of neural PDE solvers only a…
Distributed Online Non-convex Optimization with Composite Regret
Zhanhong Jiang, Aditya Balu, Xian Yeow Lee +3
Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, data/model va…
Asynchronous Training Schemes in Distributed Learning with Time Delay
Haoxiang Wang, Zhanhong Jiang, Chao Liu +3
In the context of distributed deep learning, the issue of stale weights or gradients could result in poor algorithmic performance. This issue is usually tackled by delay tolerant a…
A Deep Learning Approach to Detect Lean Blowout in Combustion Systems
Tryambak Gangopadhyay, Somnath De, Qisai Liu +3
Lean combustion is environment friendly with low NOx emissions and also provides better fuel efficiency in a combustion system. However, approaching towards lean combustion can mak…
Stochastic Conservative Contextual Linear Bandits
Jiabin Lin, Xian Yeow Lee, Talukder Jubery +3
Many physical systems have underlying safety considerations that require that the strategy deployed ensures the satisfaction of a set of constraints. Further, often we have only pa…
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
Zhanhong Jiang, Xian Yeow Lee, Sin Yong Tan +5
We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches ove…
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
Biswajit Khara, Aditya Balu, Ameya Joshi +4
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs). This approach contras…
Cross-Modal Virtual Sensing for Combustion Instability Monitoring
Tryambak Gangopadhyay, Vikram Ramanan, Satyanarayanan R Chakravarthy +1
In many cyber-physical systems, imaging can be an important but expensive or 'difficult to deploy' sensing modality. One such example is detecting combustion instability using flam…
Differentiable Spline Approximations
Minsu Cho, Aditya Balu, Ameya Joshi +6
The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differen…
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems
Xian Yeow Lee, Soumik Sarkar, Yubo Wang
Volt-var control (VVC) is the problem of operating power distribution systems within healthy regimes by controlling actuators in power systems. Existing works have mostly adopted t…
Distributed Multigrid Neural Solvers on Megavoxel Domains
Aditya Balu, Sergio Botelho, Biswajit Khara +6
We consider the distributed training of large-scale neural networks that serve as PDE solvers producing full field outputs. We specifically consider neural solvers for the generali…
Granger Causality Based Hierarchical Time Series Clustering for State Estimation
Sin Yong Tan, Homagni Saha, Margarite Jacoby +2
Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streami…
NURBS-Diff: A Differentiable Programming Module for NURBS
Anjana Deva Prasad, Aditya Balu, Harshil Shah +3
Boundary representations (B-reps) using Non-Uniform Rational B-splines (NURBS) are the de facto standard used in CAD, but their utility in deep learning-based approaches is not wel…
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Yasaman Esfandiari, Sin Yong Tan, Zhanhong Jiang +4
Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. Recently, decentralized…
3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems
Tryambak Gangopadhyay, Vikram Ramanan, Adedotun Akintayo +4
While analytical solutions of critical (phase) transitions in physical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical…
Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
Jaydeep Rade, Aditya Balu, Ethan Herron +4
Topology optimization has emerged as a popular approach to refine a component's design and increase its performance. However, current state-of-the-art topology optimization framewo…
Decentralized Deep Learning using Momentum-Accelerated Consensus
Aditya Balu, Zhanhong Jiang, Sin Yong Tan +3
We consider the problem of decentralized deep learning where multiple agents collaborate to learn from a distributed dataset. While there exist several decentralized deep learning…
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning Agents
Xian Yeow Lee, Yasaman Esfandiari, Kai Liang Tan +1
Advances in computing resources have resulted in the increasing complexity of cyber-physical systems (CPS). As the complexity of CPS evolved, the focus has shifted from traditional…
Spatiotemporal Attention for Multivariate Time Series Prediction and Interpretation
Tryambak Gangopadhyay, Sin Yong Tan, Zhanhong Jiang +2
Multivariate time series modeling and prediction problems are abundant in many machine learning application domains. Accurate interpretation of such prediction outcomes from a mach…
Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models
Sergio Botelho, Ameya Joshi, Biswajit Khara +4
Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equation…
Robustifying Reinforcement Learning Agents via Action Space Adversarial Training
Kai Liang Tan, Yasaman Esfandiari, Xian Yeow Lee +2
Adoption of machine learning (ML)-enabled cyber-physical systems (CPS) are becoming prevalent in various sectors of modern society such as transportation, industrial, and power gri…
Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning
Johnathon Shook, Tryambak Gangopadhyay, Linjiang Wu +3
Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic condition…