226 citations · 733 across the 101 of their papers we have counts for
20 papers · 1 filter
ADKO: Agentic Decentralized Knowledge Optimization
Lucas Nerone Rillo, Zhanhong Jiang, Nastaran Saadati +4
We present Agentic Decentralized Knowledge Optimization (ADKO), a framework for collaborative black-box optimization across autonomous agents that achieves sample efficiency, priva…
Towards Large Reasoning Models for Agriculture
Hossein Zaremehrjerdi, Shreyan Ganguly, Ashlyn Rairdin +17
Agricultural decision-making involves complex, context-specific reasoning, where choices about crops, practices, and interventions depend heavily on geographic, climatic, and econo…
3D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraints
Ali Rabeh, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Accurate modeling of fluid dynamics around complex geometries is critical for applications such as aerodynamic optimization and biomedical device design. While advancements in nume…
Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
Ali Rabeh, Ethan Herron, Aditya Balu +4
Rapid and accurate simulations of fluid dynamics around complicated geometric bodies are critical in a variety of engineering and scientific applications, including aerodynamics an…
Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder
Anirudha Powadi, Talukder Zaki Jubery, Michael C. Tross +2
This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phen…
Leveraging Vision Language Models for Specialized Agricultural Tasks
Muhammad Arbab Arshad, Talukder Zaki Jubery, Tirtho Roy +8
As Vision Language Models (VLMs) become increasingly accessible to farmers and agricultural experts, there is a growing need to evaluate their potential in specialized tasks. We pr…
Multi-Sensor and Multi-temporal High-Throughput Phenotyping for Monitoring and Early Detection of Water-Limiting Stress in Soybean
Sarah E. Jones, Timilehin Ayanlade, Benjamin Fallen +5
Soybean production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events. Water limiting stress, i.e. drought, emerges as a significant risk for soyb…
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…
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…
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…
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…
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…
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…
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…
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…
Encoding Invariances in Deep Generative Models
Viraj Shah, Ameya Joshi, Sambuddha Ghosal +4
Reliable training of generative adversarial networks (GANs) typically require massive datasets in order to model complicated distributions. However, in several applications, traini…
Interpretable deep learning for guided structure-property explorations in photovoltaics
Balaji Sesha Sarath Pokuri, Sambuddha Ghosal, Apurva Kokate +2
The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very e…
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
Xian Yeow Lee, Aditya Balu, Daniel Stoecklein +2
Microfluidic devices are utilized to control and direct flow behavior in a wide variety of applications, particularly in medical diagnostics. A particularly popular form of microfl…
A Bayesian Network approach to County-Level Corn Yield Prediction using historical data and expert knowledge
Vikas Chawla, Hsiang Sing Naik, Adedotun Akintayo +4
Crop yield forecasting is the methodology of predicting crop yields prior to harvest. The availability of accurate yield prediction frameworks have enormous implications from multi…
Deep Action Sequence Learning for Causal Shape Transformation
Kin Gwn Lore, Daniel Stoecklein, Michael Davies +2
Deep learning became the method of choice in recent year for solving a wide variety of predictive analytics tasks. For sequence prediction, recurrent neural networks (RNN) are ofte…