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Found 10 papers, 2 papers with code
Distributed Reinforcement Learning for Molecular Design: Antioxidant case
The proposed models are trained with a proprietary antioxidant dataset.
STRIDE: Structure-guided Generation for Inverse Design of Molecules
Machine learning and especially deep learning has had an increasing impact on molecule and materials design.
Deep Compressed Learning for 3D Seismic Inversion
We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources.
Joint inversion of Time-Lapse Surface Gravity and Seismic Data for Monitoring of 3D CO$_2$ Plumes via Deep Learning
We introduce a fully 3D, deep learning-based approach for the joint inversion of time-lapse surface gravity and seismic data for reconstructing subsurface density and velocity models.
Learning CO$_2$ plume migration in faulted reservoirs with Graph Neural Networks
Accurately capturing the impact of faults on CO$_2$ plume migration remains a challenge for many existing deep learning surrogate models based on Convolutional Neural Networks (CNNs) or Neural Operators.
ParticleGrid: Enabling Deep Learning using 3D Representation of Materials
We propose $\textit{ParticleGrid}$, a SIMD-optimized library for 3D structures, that is designed for deep learning applications and to seamlessly integrate with deep learning frameworks.
Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO$_2$ Plumes via Deep Learning
We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties.
Encoder-Decoder Architecture for 3D Seismic Inversion
This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys.
Deep Neural Network Learning with Second-Order Optimizers -- a Practical Study with a Stochastic Quasi-Gauss-Newton Method
Training in supervised deep learning is computationally demanding, and the convergence behavior is usually not fully understood.
Learning with a Wasserstein Loss
In this paper we develop a loss function for multi-label learning, based on the Wasserstein distance.
