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Found 67 papers, 33 papers with code
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.
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 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.
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.
Creativity in Robot Manipulation with Deep Reinforcement Learning
This capability of DRL generates more human-like behaviour and intelligence when applied to the robots.
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.
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.
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
We developed a general deep learning framework, FluidGAN, capable of learning and predicting time-dependent convective flow coupled with energy transport.
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.
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 #2 on
Classification
on CWRU Bearing Dataset
(using extra training data)
Data-driven Identification of 2D Partial Differential Equations using extracted physical features
Many scientific phenomena are modeled by Partial Differential Equations (PDEs).
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.
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.
Learning Lagrangian Fluid Dynamics with Graph Neural Networks
We present a data-driven model for fluid simulation under Lagrangian representation.
Airfoil GAN: Encoding and Synthesizing Airfoils for Aerodynamic 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.
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.
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.
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).
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.
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.
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.
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.
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.
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.
Graph Neural Networks Accelerated Molecular Dynamics
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter.
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.
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.
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction
Machine learning (ML) models have been widely successful in the prediction of material properties.
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.
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.
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.
TransPolymer: a Transformer-based language model for polymer property predictions
Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer.
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.
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.
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.
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.
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.
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.
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.
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
These results highlight the potential for leveraging denoise pretraining approaches to build more generalizable neural potentials for complex molecular systems.
Transformer-based Planning for Symbolic Regression
Unlike conventional decoding strategies, TPSR enables the integration of non-differentiable feedback, such as fitting accuracy and complexity, as external sources of knowledge into the transformer-based equation generation process.
Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
Overall, our deep learning model is a fast, flexible, and accurate surrogate model to predict ion concentration profiles in nanoconfinement.
Physics Informed Token Transformer for Solving Partial Differential Equations
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering.
OpenVR: Teleoperation for Manipulation
Across the robotics field, quality demonstrations are an integral part of many control pipelines.
Scalable Transformer for PDE Surrogate Modeling
These sub-functions are then evaluated and used to compute the instance-based kernel with an axial factorized scheme.
Hyena Neural Operator for Partial Differential Equations
The Hyena operator is an operation that enjoys sub-quadratic complexity and state space model to parameterize long convolution that enjoys a global receptive field.
Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction
Then, the latent representations of the input data, produced from the pretrained autoencoder, is processed with a distinct inference head to infer either the fluid category (classification) or the fluid viscosity (regression) in a time-resolved manner.
PeptideBERT: A Language Model based on Transformers for Peptide Property Prediction
In this work, inspired by recent progress in Large Language Models (LLMs), we introduce PeptideBERT, a protein language model for predicting three key properties of peptides (hemolysis, solubility, and non-fouling).
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties Prediction
We extend this paradigm by utilizing LLMs for material property prediction by introducing our model Materials Informatics Transformer (MatInFormer).
SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation
Deformable object manipulation presents a unique set of challenges in robotic manipulation by exhibiting high degrees of freedom and severe self-occlusion.
One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers
We achieve this goal by using linear transforms to augment the single demonstration, generating a set of trajectories for a wide range of initial conditions.
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
A text based description of 326000 molecules were collected using ChatGPT and used to train LLM to learn the representation of molecules.
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training
To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings.
GPCR-BERT: Interpreting Sequential Design of G Protein Coupled Receptors Using Protein Language Models
In this paper, we developed the GPCR-BERT model for understanding the sequential design of G Protein-Coupled Receptors (GPCRs).
Multi-scale Time-stepping of Partial Differential Equations with Transformers
Developing fast surrogates for Partial Differential Equations (PDEs) will accelerate design and optimization in almost all scientific and engineering applications.
Inexpensive High Fidelity Melt Pool Models in Additive Manufacturing Using Generative Deep Diffusion
Defects in laser powder bed fusion (L-PBF) parts often result from the meso-scale dynamics of the molten alloy near the laser, known as the melt pool.
FaultFormer: Pretraining Transformers for Adaptable Bearing Fault Classification
In this work, we present pretraining and fine-tuning frameworks for identifying bearing faults based on transformer models.
Characterization of Phosphorylated Tau-Microtubule complex with Molecular Dynamics (MD) simulation
Alzheimer's Disease (AD), a neurodegenerative disorder, is reported as one of the most severe health and socioeconomic problems in current public health.
PICL: Physics Informed Contrastive Learning for Partial Differential Equations
A combination of physics-informed system evolution and latent-space model output are anchored to input data and used in our distance function.
Pretraining Strategy for Neural Potentials
We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems.
Inpainting Computational Fluid Dynamics with Deep Learning
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics.
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs).
SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy
Manipulating deformable objects remains a challenge within robotics due to the difficulties of state estimation, long-horizon planning, and predicting how the object will deform given an interaction.
Visuo-Tactile Pretraining for Cable Plugging
Our results show that by pretraining with tactile information, the performance of a non-tactile agent can be significantly improved, reaching a level on par with visuo-tactile agents.
Masked Autoencoders are PDE Learners
We hope that masked pretraining can emerge as a unifying method across large, unlabeled, and heterogeneous datasets to learn latent physics at scale.
IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins (IDP) Using Large Language Models
In this study, we introduce IDP-Bert model, a deep-learning architecture leveraging Transformers and Protein Language Models (PLMs) to map sequences directly to IDPs properties.
AlloyBERT: Alloy Property Prediction with Large Language Models
The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field.
