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Found 42 papers, 17 papers with code
Transformer-based Planning for Symbolic Regression
To address this, we propose TPSR, a Transformer-based Planning strategy for Symbolic Regression that incorporates Monte Carlo Tree Search into the transformer decoding process.
Denoise Pre-training on Non-equilibrium Molecules for Accurate and Transferable Neural Potentials
These results highlight the potential for leveraging denoise pre-training approaches to build more generalizable neural potentials for complex molecular systems.
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics
Current machine learning techniques often require substantial computational cost for training data generation, and are restricted in scope to the training data flow regime.
A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dynamics data.
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property Prediction
Furthermore, we revealed that MOFormer can be more data-efficient on quantum-chemical property prediction than structure-based CGCNN when training data is limited.
Predicting CO$_2$ Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks
Fingerprint works on graph structure at the feature extraction stage, while GNNs directly handle molecule structure in both the feature extraction and model prediction stage.
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning
The use of human demonstrations in reinforcement learning has proven to significantly improve agent performance.
MAN: Multi-Action Networks Learning
In this work, we introduce a Deep Reinforcement Learning (DRL) algorithm call Multi-Action Networks (MAN) Learning that addresses the challenge of high-dimensional large discrete action spaces.
Graph Neural Networks for Molecules
Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems.
TransPolymer: a Transformer-based Language Model for Polymer Property Predictions
The model learns expressive representations by pretraining on a large unlabeled dataset via masked language modeling, followed by finetuning the model on downstream datasets concerning various polymer properties.
Mechanical Properties Prediction in Metal Additive Manufacturing Using Machine Learning
Nonetheless, Machine learning (ML) methods, which are more flexible and cost-effective solutions, can be utilized to predict mechanical properties based on the processing parameters and material properties.
Surrogate Modeling of Melt Pool Thermal Field using Deep Learning
In this work, we create three datasets of single-trail processes using Flow-3D and use them to train a convolutional neural network capable of predicting the behavior of the three-dimensional thermal field of the melt pool solely by taking three parameters as input: laser power, laser velocity, and time step.
Transformer for Partial Differential Equations' Operator Learning
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions.
Deep-Learned Generators of Porosity Distributions Produced During Metal Additive Manufacturing
Laser Powder Bed Fusion has become a widely adopted method for metal Additive Manufacturing (AM) due to its ability to mass produce complex parts with increased local control.
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Machine learning (ML) models have been widely successful in the prediction of material properties.
Improving Molecular Contrastive Learning via Faulty Negative Mitigation and Decomposed Fragment Contrast
On most benchmarks, the generic GNN pre-trained by iMolCLR rivals or even surpasses supervised learning models with sophisticated architecture designs and engineered features.
MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning
In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization.
Graph Neural Networks Accelerated Molecular Dynamics
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter.
AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning
Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data augmentation library for chemical structures.
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences
Our model, Temporal Point cloud Upsampling GAN (TPU-GAN), can implicitly learn the underlying temporal coherence from point cloud sequence, which in turn guides the generator to produce temporally coherent output.
Prototype memory and attention mechanisms for few shot image generation
To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model.
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations
Reduced Order Modelling (ROM) has been widely used to create lower order, computationally inexpensive representations of higher-order dynamical systems.
Dominant motion identification of multi-particle system using deep learning from video
In this work, we provide a deep-learning framework that extracts relevant information from real-world videos of highly stochastic systems, with no prior knowledge and distills the underlying governing equation representing the system.
An Energy-Saving Snake Locomotion Gait Policy Obtained Using Deep Reinforcement Learning
Comparing to conventional control strategies, the snake robots controlled by the trained PPO agent can achieve faster movement and more energy-efficient locomotion gait.
Molecular Contrastive Learning of Representations via Graph Neural Networks
In this work, we present MolCLR: Molecular Contrastive Learning of Representations via Graph Neural Networks (GNNs), a self-supervised learning framework that leverages large unlabeled data (~10M unique molecules).
Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning
Powder-based additive manufacturing techniques provide tools to construct intricate structures that are difficult to manufacture using conventional methods.
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination
Structure and geometry optimization of nanopores on such materials is beneficial for their performances in real-world engineering applications, like water desalination.
Airfoil GAN: Encoding and Synthesizing Airfoils forAerodynamic-aware Shape Optimization
Our model can (1) encode the existing airfoil into a latent vector and reconstruct the airfoil from that, (2) generate novel airfoils by randomly sampling the latent vectors and mapping the vectors to the airfoil coordinate domain, and (3) synthesize airfoils with desired aerodynamic properties by optimizing learned features via a genetic algorithm.
Learning Lagrangian Fluid Dynamics with Graph Neural Networks
We present a data-driven model for fluid simulation under Lagrangian representation.
Graph Convolutional Neural Networks for Body Force Prediction
The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements.
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS Data
Large Eddy Simulation (LES) presents a more computationally efficient approach for solving fluid flows on lower-resolution (LR) grids but results in an overall reduction in solution fidelity.
Data-driven Identification of 2D Partial Differential Equations using extracted physical features
Many scientific phenomena are modeled by Partial Differential Equations (PDEs).
FaultNet: A Deep Convolutional Neural Network for bearing fault classification
In this work, we analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods and coupling them with machine learning techniques to classify different types of bearing faults.
Ranked #1 on
Classification
on CWRU Bearing Dataset
Orbital Graph Convolutional Neural Network for Material Property Prediction
We benchmarked the performance of the OGCNN model with that of: 1) the crystal graph convolutional neural network (CGCNN), 2) other state-of-the-art descriptors for material representations including Many-body Tensor Representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), and 3) other conventional regression machine learning algorithms where different crystal featurization methods have been used.
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
We developed a general deep learning framework, FluidGAN, that is capable of learning and predicting time-dependent convective flow coupled with energy transport.
Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody.
Effects of sparse rewards of different magnitudes in the speed of learning of model-based actor critic methods
Actor critic methods with sparse rewards in model-based deep reinforcement learning typically require a deterministic binary reward function that reflects only two possible outcomes: if, for each step, the goal has been achieved or not.
Creativity in Robot Manipulation with Deep Reinforcement Learning
This capability of DRL generates more human-like behaviour and intelligence when applied to the robots.
Weakly-Supervised Deep Learning of Heat Transport via Physics Informed Loss
In typical machine learning tasks and applications, it is necessary to obtain or create large labeled datasets in order to to achieve high performance.
Deep Learning Phase Segregation
Phase segregation, the process by which the components of a binary mixture spontaneously separate, is a key process in the evolution and design of many chemical, mechanical, and biological systems.
Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes
Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the $\mu$ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states.
Deep Learning the Physics of Transport Phenomena
We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning.
