Search Results for author:
Found 15 papers, 5 papers with code
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
We extend the capabilities of PARC to simulate unsteady, transient, and advection-dominant systems.
Projected Generative Diffusion Models for Constraint Satisfaction
Generative diffusion models excel at robustly synthesizing coherent content from raw noise through a sequential process.
Challenges and opportunities for machine learning in multiscale computational modeling
Recently, machine learning (ML) has emerged as a promising solution that can either serve as a surrogate for, accelerate or augment traditional numerical methods.
Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions
Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation.
A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials
We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock-initiated EM microstructures.
Federated Learning on Adaptively Weighted Nodes by Bilevel Optimization
We propose a federated learning method with weighted nodes in which the weights can be modified to optimize the model's performance on a separate validation set.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials
The thermo-mechanical response of shock-initiated energetic materials (EM) is highly influenced by their microstructures, presenting an opportunity to engineer EM microstructure in a "materials-by-design" framework.
On the role of depth predictions for 3D human pose estimation
Furthermore, our system can be combined with an off-the-shelf 2d pose detector and a depth map predictor to perform 3d pose estimation in the wild.
Incremental ELMVIS for unsupervised learning
An incremental version of the ELMVIS+ method is proposed in this paper.
Multi-scale Embedded CNN for Music Tagging (MsE-CNN)
Convolutional neural networks (CNN) recently gained notable attraction in a variety of machine learning tasks: including music classification and style tagging.
Deep segmentation networks predict survival of non-small cell lung cancer
Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value.
ZerNet: Convolutional Neural Networks on Arbitrary Surfaces via Zernike Local Tangent Space Estimation
In this paper, we propose a novel formulation to extend CNNs to two-dimensional (2D) manifolds using orthogonal basis functions, called Zernike polynomials.
Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks
The problem is well-known to be of a paramount clinical importance, but yet, traditional ConvNets cannot be applied due to the manifold structure of the data, neither does the state-of-the-art geometric ConvNets perform well.
Embedded Spectral Descriptors: Learning the point-wise correspondence metric via Siamese neural networks
In this paper, it is proposed a method to improve the similarity metric of spectral descriptors for correspondence matching problems.
