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Found 20 papers, 4 papers with code
An Operator Learning Framework for Spatiotemporal Super-resolution of Scientific Simulations
In numerous contexts, high-resolution solutions to partial differential equations are required to capture faithfully essential dynamics which occur at small spatiotemporal scales, but these solutions can be very difficult and slow to obtain using traditional methods due to limited computational resources.
RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows
Physics-informed neural networks (PINNs) provide a framework to build surrogate models for dynamical systems governed by differential equations.
A Neural ODE Interpretation of Transformer Layers
Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks.
Demystifying the Data Need of ML-surrogates for CFD Simulations
Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties.
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids.
A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network.
EMVLight: A Decentralized Reinforcement Learning Framework for Efficient Passage of Emergency Vehicles
EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real time as it travels through the traffic network.
Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models
In this paper, we introduce a differentiable contact model, which can capture contact mechanics: frictionless/frictional, as well as elastic/inelastic.
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data
The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks.
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension
Value function based reinforcement learning (RL) algorithms, for example, $Q$-learning, learn optimal policies from datasets of actions, rewards, and state transitions.
Frequency-compensated PINNs for Fluid-dynamic Design Problems
We demonstrate this approach by predicting simulation results over out of range time interval and for novel design conditions.
Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning
In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories.
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications.
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
Here we propose an interpretable network trained on the jet spectrum $S_{2}(R)$ which is a two-point correlation function of the jet constituents.
InverseNet: Solving Inverse Problems with Splitting Networks
We propose a new method that uses deep learning techniques to solve the inverse problems.
Convolutional Neural Knowledge Graph Learning
In this paper, we try to learn more complex connections between entities and relationships.
Alternating direction method of multipliers for regularized multiclass support vector machines
A lot of effort has been put to generalize the binary SVM to multiclass SVM (MSVM) which are more complex problems.
Proximal gradient method for huberized support vector machine
The Support Vector Machine (SVM) has been used in a wide variety of classification problems.
HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection
We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available.
