Search Results for author:
Found 38 papers, 10 papers with code
Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
This study addresses this gap by benchmarking diverse SciML models, including neural operators and vision transformer-based foundation models, for fluid flow prediction over intricate geometries.
STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology
We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud while enforcing topological constraints (such as having a single connected component).
Robust soybean seed yield estimation using high-throughput ground robot videos
These images are processed through the P2PNet-Yield model, a deep learning framework where we combined a Feature Extraction Module (the backbone of the P2PNet-Soy) and a Yield Regression Module to estimate seed yields of soybean plots.
FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometries
FlowBench contains over 10K data samples, with each sample the outcome of a fully resolved, direct numerical simulation using a well-validated simulator framework designed for modeling transport phenomena in complex geometries.
AgGym: An agricultural biotic stress simulation environment for ultra-precision management planning
We further demonstrate that deep reinforcement learning (RL) policies can be trained using AgGym for designing ultra-precise biotic stress mitigation strategies with potential to increase yield recovery with less chemicals and lower cost.
AgEval: A Benchmark for Zero-Shot and Few-Shot Plant Stress Phenotyping with Multimodal LLMs
Plant stress phenotyping traditionally relies on expert assessments and specialized models, limiting scalability in agriculture.
Slice-100K: A Multimodal Dataset for Extrusion-based 3D Printing
G-code (Geometric code) or RS-274 is the most widely used computer numerical control (CNC) and 3D printing programming language.
Class-specific Data Augmentation for Plant Stress Classification
This research represents an advancement in automated data augmentation strategies for plant stress classification, particularly in the context of confounding datasets.
DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models
This DIMAT paradigm presents a new opportunity for the future decentralized learning, enhancing its adaptability to real-world with sparse and light-weight communication and computation.
Evaluating Neural Radiance Fields (NeRFs) for 3D Plant Geometry Reconstruction in Field Conditions
We evaluate different Neural Radiance Fields (NeRFs) techniques for the 3D reconstruction of plants in varied environments, from indoor settings to outdoor fields.
Latent Diffusion Models for Structural Component Design
Specifically, we employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions.
Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension
3D printing or additive manufacturing is a revolutionary technology that enables the creation of physical objects from digital models.
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
Current state-of-the-art methods for text-to-shape generation either require supervised training using a labeled dataset of pre-defined 3D shapes, or perform expensive inference-time optimization of implicit neural representations.
Out-of-distribution detection algorithms for robust insect classification
One of the primary emphasis of researchers is to implement identification and classification models in the real agriculture fields, which is challenging because input images that are wildly out of the distribution (e. g., images like vehicles, animals, humans, or a blurred image of an insect or insect class that is not yet trained on) can produce an incorrect insect classification.
SpecXAI -- Spectral interpretability of Deep Learning Models
Deep learning is becoming increasingly adopted in business and industry due to its ability to transform large quantities of data into high-performing models.
Neural PDE Solvers for Irregular Domains
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention.
Distributed Online Non-convex Optimization with Composite Regret
To address these two issues, we propose a novel composite regret with a new network regret-based metric to evaluate distributed online optimization algorithms.
Concept Activation Vectors for Generating User-Defined 3D Shapes
We explore the interpretability of 3D geometric deep learning models in the context of Computer-Aided Design (CAD).
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
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 over iterations.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+4
Differentiable Spline Approximations
Overall, we show that leveraging this redesigned Jacobian in the form of a differentiable "layer" in predictive models leads to improved performance in diverse applications such as image segmentation, 3D point cloud reconstruction, and finite element analysis.
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs).
Distributed Multigrid Neural Solvers on Megavoxel Domains
We specifically consider neural solvers for the generalized 3D Poisson equation over megavoxel domains.
NURBS-Diff: A Differentiable Programming Module for NURBS
These derivatives are used to define an approximate Jacobian used for performing the "backward" evaluation to train the deep learning models.
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i. e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP).
Differentiable Programming for Piecewise Polynomial Functions
The paradigm of differentiable programming has considerably enhanced the scope of machine learning via the judicious use of gradient-based optimization.
Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
We achieve this by training multiple networks, each learning a different step of the overall topology optimization methodology, making the framework more consistent with the topology optimization algorithm.
Decentralized Deep Learning using Momentum-Accelerated Consensus
In this context, we propose and analyze a novel decentralized deep learning algorithm where the agents interact over a fixed communication topology (without a central server).
A Fast Saddle-Point Dynamical System Approach to Robust Deep Learning
Under the assumptions that the cost function is convex and uncertainties enter concavely in the robust learning problem, we analytically show that our algorithm converges asymptotically to the robust optimal solution under a general adversarial budget constraints as induced by $\ell_p$ norm, for $1\leq p\leq \infty$.
On Higher-order Moments in Adam
In this paper, we investigate the popular deep learning optimization routine, Adam, from the perspective of statistical moments.
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning
A particularly popular form of microfluidics -- called inertial microfluidic flow sculpting -- involves placing a sequence of pillars to controllably deform an initial flow field into a desired one.
3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys
In this paper, we explore a deep convolutional neural-network based approach to develop the atomistic potential for such complex alloys to investigate materials for insights into controlling properties.
Multi-level 3D CNN for Learning Multi-scale Spatial Features
The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object.
On Consensus-Optimality Trade-offs in Collaborative Deep Learning
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality.
A Forward-Backward Approach for Visualizing Information Flow in Deep Networks
We introduce a new, systematic framework for visualizing information flow in deep networks.
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.
Collaborative Deep Learning in Fixed Topology Networks
There is significant recent interest to parallelize deep learning algorithms in order to handle the enormous growth in data and model sizes.
A Machine-Learning Framework for Design for Manufacturability
this is a duplicate submission(original is arXiv:1612. 02141).
Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.
