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Found 23 papers, 2 papers with code
Estimating the Distribution of Parameters in Differential Equations with Repeated Cross-Sectional Data
With RCS data, we found that traditional methods for parameter estimation in differential equations, such as using mean values of time trajectories or Gaussian Process-based trajectory generation, have limitations in estimating the shape of parameter distributions, often leading to a significant loss of data information.
Sobolev Training for Operator Learning
This study investigates the impact of Sobolev Training on operator learning frameworks for improving model performance.
Learning time-dependent PDE via graph neural networks and deep operator network for robust accuracy on irregular grids
There has been growing interest in models that learn the operator from the parameters of a partial differential equation (PDE) to the corresponding solutions.
HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
This study proposes HyperDeepONet, which uses the expressive power of the hypernetwork to enable the learning of a complex operator with a smaller set of parameters.
opPINN: Physics-Informed Neural Network with operator learning to approximate solutions to the Fokker-Planck-Landau equation
We propose a hybrid framework opPINN: physics-informed neural network (PINN) with operator learning for approximating the solution to the Fokker-Planck-Landau (FPL) equation.
Enhanced Physics-Informed Neural Networks with Augmented Lagrangian Relaxation Method (AL-PINNs)
By employing Augmented Lagrangian relaxation, the constrained optimization problem becomes a sequential max-min problem so that the learnable parameters $\lambda$ adaptively balance each loss component.
Pseudo-Differential Neural Operator: Generalized Fourier Neural Operator for Learning Solution Operators of Partial Differential Equations
We combine the PDIO with the neural operator to develop a \textit{pseudo-differential neural operator} (PDNO) and learn the nonlinear solution operator of PDEs.
Solving PDE-constrained Control Problems Using Operator Learning
Once the surrogate model is trained in Phase 1, the optimal control can be inferred in Phase 2 without intensive computations.
Machine Learning-Based COVID-19 Patients Triage Algorithm using Patient-Generated Health Data from Nationwide Multicenter Database
The prompt severity assessment model for managing infectious people has been attained through using a nationwide dataset.
Lagrangian dual framework for conservative neural network solutions of kinetic equations
In this paper, we propose a novel conservative formulation for solving kinetic equations via neural networks.
Prior Preference Learning from Experts:Designing a Reward with Active Inference
In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them.
Traveling Wave Solutions of Partial Differential Equations via Neural Networks
This paper focuses on how to approximate traveling wave solutions for various kinds of partial differential equations via artificial neural networks.
Numerical Analysis Numerical Analysis Analysis of PDEs
Sobolev Training for the Neural Network Solutions of PDEs
Inspired by the recent studies that incorporate derivative information for the training of neural networks, we develop a loss function that guides a neural network to reduce the error in the corresponding Sobolev space.
Prior Preference Learning From Experts: Designing A Reward with Active Inference
In this paper, we claim that active inference can be interpreted using reinforcement learning (RL) algorithms and find a theoretical connection between them.
The model reduction of the Vlasov-Poisson-Fokker-Planck system to the Poisson-Nernst-Planck system via the Deep Neural Network Approach
The model reduction of a mesoscopic kinetic dynamics to a macroscopic continuum dynamics has been one of the fundamental questions in mathematical physics since Hilbert's time.
Posting Bot Detection on Blockchain-based Social Media Platform using Machine Learning Techniques
Comparing the F1-scores, the features we created outperformed the features used for bot detection on Facebook and Twitter.
Trend to Equilibrium for the Kinetic Fokker-Planck Equation via the Neural Network Approach
The issue of the relaxation to equilibrium has been at the core of the kinetic theory of rarefied gas dynamics.
Option Compatible Reward Inverse Reinforcement Learning
In this paper, we solve a hierarchical inverse reinforcement learning problem within the options framework, which allows us to utilize intrinsic motivation of the expert demonstrations.
White Box Network: Obtaining a right composition ordering of functions
The universality of a neural network enables the approximation of any type of continuous functions.
NEAR: Neighborhood Edge AggregatoR for Graph Classification
However, past GNN algorithms based on 1-hop neighborhood neural message passing are exposed to a risk of loss of information on local structures and relationships.
Deep Neural Network Approach to Forward-Inverse Problems
That is, we provide a unified framework of DNN architecture that approximates an analytic solution and its model parameters simultaneously.
Local Stability and Performance of Simple Gradient Penalty $\mu$-Wasserstein GAN
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.
Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution.
