226 citations · 733 across the 101 of their papers we have counts for
110 papers
From Sparse AFM Observations to Probabilistic Macroscale Mechanics: A Generative-Physics Framework for Plant Cell Walls
Samundra Karki, Angona Biswas, Nabila Masud +3
Nanoscale imaging of developing plant cell walls is expensive, and a limited number of atomic force microscopy (AFM) scans cannot capture the full structural variability of the wal…
A Vision-Language Model (VLM)-based Pipeline for End-to-End Procedural Modeling of Field-Grown Maize from Point Clouds
Mozhgan Hadadi, Talukder Z. Jubery, Adarsh Krishnamurthy +1
Editable 3D models of field-grown crops support high-throughput phenotyping and in silico breeding trials, but building them from scanned point clouds requires organ-level segmenta…
Semi-automated reconstruction of indoor geometry from 360-degree video for CFD-based airflow analysis in classrooms
Dhruv Gamdha, James Afful, Shambhavi Joshi +3
Computational Fluid Dynamics (CFD) is widely used to evaluate ventilation and contaminant transport in occupied buildings, but deployment at scale is limited by three bottlenecks:…
Automated Maize Ear Phenotyping Using 3D Reconstructions
Ritwesh A. Kumar, Som Tripathi, Peja Matthews +5
Maize kernel traits such as row number, kernels per row, and kernel size vary largely for genetic reasons and are consistently associated with regions of the genome that influence…
AI-enabled Low-Cost 3D Maize Ear Morphometry Platform at Breeding Scale
Therin Young, Elijah Rodriguez, Lisa Coffey +4
Maize ear geometry (length, width, curvature, and volume) is closely tied to yield and grain-filling outcomes, but existing high-throughput phenotyping pipelines remain constrained…
A Helmholtz-Leray projection method with variational multiscale stabilization for the Navier-Stokes equations
Biswajit Khara, Suresh Murugaiyan, Makrand Khanwale +1
The Galerkin finite element formulation of the incompressible Navier-Stokes equations presents two principal challenges: maintaining stable velocity-pressure coupling and controlli…
AI-integrated models for assessing agricultural resilience
Joshua R. Waite, Dana Golden, Brett Indelicato +8
Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems. We develop an AI-powered tool that integrates economic models (GTAP) with…
Procedural Volumetric Modeling of Plant Branching Structures for Finite Element Analysis
Ajith Moola, Prashant Kumar Gupta, Baskar Ganapathysubramanian +1
Precision agriculture, smart breeding, and agricultural robotics require accurate and automated plant modeling. These models provide high-fidelity three-dimensional (3D) representa…
Uncertainty-aware neural emulation reveals climate-resilient maize traits at scale
Mojdeh Saadati, Juan Panelo, Gustavo Visentini +3
Global food security depends on predicting crop responses to climate variability, yet process based crop models remain too computationally expensive for large scale exploration of…
SAGE: Scalable Agentic Grounded Evaluation for Crop Disease Diagnosis
Muhammad Arbab Arshad, Tirtho Roy, Yanben Shen +7
Plant disease diagnosis is critical for food security, yet training disease-recognition models that generalize across crops, pathogens, and field conditions remains challenging bec…
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…
Neural-Network-based Viscosity Closure for Non-Newtonian Multiphase Flows
Suresh Murugaiyan, Claire L. Nelson, Dhruv Gamdha +11
Materials used in polymer-based additive manufacturing processes, such as Digital Light Processing (DLP) and direct ink writing (DIW), typically exhibit non-Newtonian rheology. Car…
GENIE: Gram-Eigenmode INR Editing with Closed-Form Geometry Updates
Samundra Karki, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Implicit Neural Representations (INRs) provide compact models of geometry, but it is unclear when their learned shapes can be edited without retraining. We show that the Gram opera…
HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs
Kibon Ku, Talukder Z. Jubery, Adarsh Krishnamurthy +1
Advances in hyperspectral imaging (HSI) and 3D reconstruction have enabled accurate, high-throughput characterization of agricultural produce quality and plant phenotypes, both ess…
Field conserving adaptive mesh refinement (AMR) scheme on massively parallel adaptive octree meshes
Kumar Saurabh, Makrand A. Khanwale, Masado Ishii +2
Adaptive mesh refinement (AMR) is widely used to efficiently resolve localized features in time-dependent partial differential equations (PDEs) by selectively refining and coarseni…
Neural Geometry for PDEs: Regularity, Stability, and Convergence Guarantees
Samundra Karki, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Implicit Neural Representations (INRs) have emerged as a powerful tool for geometric representation, yet their suitability for physics-based simulation remains underexplored. While…
Towards smart canopies: Algorithmic design of maize canopy architectures that maximize light use efficiency
Nasla Saleem, Talukder Zaki Jubery, Yan Zhou +4
We present a computational framework that integrates functional-structural plant modeling (FSPM) with an evolutionary algorithm to optimize three-dimensional maize canopy architect…
FloraForge: LLM-Assisted Procedural Generation of Editable and Analysis-Ready 3D Plant Geometric Models For Agricultural Applications
Mozhgan Hadadi, Talukder Z. Jubery, Patrick S. Schnable +4
Accurate 3D plant models are crucial for computational phenotyping and physics-based simulation; however, current approaches face significant limitations. Learning-based reconstruc…
Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks
Ali Rabeh, Suresh Murugaiyan, Adarsh Krishnamurthy +1
Fast, geometry-generalizing surrogates for unsteady flow remain challenging. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for mod…
A Semi-Implicit Variational Multiscale Formulation for the Incompressible Navier-Stokes Equations via Exact Adjoint Linearization
Biswajit Khara, Suresh Murugaiyan, Suriya Dhakshinamoorthy +3
A semi-implicit, residual-based variational multiscale (VMS) formulation is developed for the incompressible Navier-Stokes equations. The convection term is linearized using an ext…
Crossmodal learning for Crop Canopy Trait Estimation
Timilehin T. Ayanlade, Anirudha Powadi, Talukder Z. Jubery +2
Recent advances in plant phenotyping have driven widespread adoption of multi sensor platforms for collecting crop canopy reflectance data. This includes the collection of heteroge…
InSpecLearn4SDL: Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
Ankush Kumar Mishra, Jacob P. Mauthe, Nicholas Luke +2
To accelerate materials discovery using self-driving labs (SDLs), we present a machine learning pipeline that predicts the electrical conductivity of doped conjugated polymers usin…
Mechanics Simulation with Implicit Neural Representations of Complex Geometries
Samundra Karki, Ming-Chen Hsu, Adarsh Krishnamurthy +1
Implicit Neural Representations (INRs), characterized by neural network-encoded signed distance fields, provide a powerful means to represent complex geometries continuously and ef…
TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Models
Shivani Chiranjeevi, Hossein Zaremehrjerdi, Zi K. Deng +9
The rapid global loss of biodiversity, particularly among insects, represents an urgent ecological crisis. Current methods for insect species discovery are manual, slow, and severe…