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Found 14 papers, 3 papers with code
Discovering State Variables Hidden in Experimental Data
All physical laws are described as relationships between state variables that give a complete and non-redundant description of the relevant system dynamics.
A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation
Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e. g., machine learning, ML), while each suffers from either deficient physics or small data.
The Discovery of Dynamics via Linear Multistep Methods and Deep Learning: Error Estimation
We put forward error estimates for these methods using the approximation property of deep networks.
A fast two-stage algorithm for non-negative matrix factorization in streaming data
In the second stage, an interior point method is adopted to accelerate the local convergence.
Optimization and Control Numerical Analysis Numerical Analysis 65K10, 90C26 G.1.6; F.2.1
Physics-Informed Deep Learning for Traffic State Estimation
This paper focuses on highway TSE with observed data from loop detectors, using traffic density as the traffic variables.
Dynamic driving and routing games for autonomous vehicles on networks: A mean field game approach
In this paper, we aim to develop a game-theoretic model to solve for AVs's optimal driving strategies of velocity control in the interior of a road link and route choice at a junction node.
Autonomous Vehicles
Decision Making
Optimization and Control
Systems and Control
Systems and Control
A non-cooperative meta-modeling game for automated third-party calibrating, validating, and falsifying constitutive laws with parallelized adversarial attacks
The evaluation of constitutive models, especially for high-risk and high-regret engineering applications, requires efficient and rigorous third-party calibration, validation and falsification.
Discovery of Dynamics Using Linear Multistep Methods
Linear multistep methods (LMMs) are popular time discretization techniques for the numerical solution of differential equations.
Censored Stable Subordinators and Fractional Derivatives
Based on the popular Caputo fractional derivative of order $\beta$ in $(0, 1)$, we define the censored fractional derivative on the positive half-line $\mathbb R_+$.
Classical Analysis and ODEs Probability 26A33, 60G52, 60G40
A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
We introduce a multi-agent meta-modeling game to generate data, knowledge, and models that make predictions on constitutive responses of elasto-plastic materials.
Stochastic Training of Residual Networks: a Differential Equation Viewpoint
During the last few years, significant attention has been paid to the stochastic training of artificial neural networks, which is known as an effective regularization approach that helps improve the generalization capability of trained models.
Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
Nonlocal neural networks have been proposed and shown to be effective in several computer vision tasks, where the nonlocal operations can directly capture long-range dependencies in the feature space.
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Time series prediction has been studied in a variety of domains.
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization.
Ranked #6 on
Text Summarization
on DUC 2004 Task 1
