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Found 18 papers, 4 papers with code
Evaluating NeRFs for 3D Plant Geometry Reconstruction in Field Conditions
We evaluate different Neural Radiance Fields (NeRFs) techniques for reconstructing (3D) 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.
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.
3D Reconstruction of Protein Complex Structures Using Synthesized Multi-View AFM Images
These multi-view images can help train the neural network to predict the 3D structure of protein complexes.
Neural PDE Solvers for Irregular Domains
Neural network-based approaches for solving partial differential equations (PDEs) have recently received special attention.
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).
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.
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.
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.
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.
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.
