Cover Image

A Bayesian Approach for Discovering Time- Delayed Differential Equation from Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chowdhury, Debangshu, Chakraborty, Souvik
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Discovering Interpretable Ordinary Differential Equations from Noisy Data
by: Golder, Rahul, et al.
Published: (2025)

Discovering governing equation in structural dynamics from acceleration-only measurements
by: Alvares, Calvin, et al.
Published: (2024)

SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
by: Garg, Shailesh, et al.
Published: (2026)

Reinforcement Learning Closures for Underresolved Partial Differential Equations using Synthetic Data
by: Heimbach, Lothar, et al.
Published: (2025)

CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis
by: Garg, Shailesh, et al.
Published: (2026)

Projected Neural Differential Equations for Learning Constrained Dynamics
by: White, Alistair, et al.
Published: (2024)

CortiNet: A Physics-Perception Hybrid Cortical-Inspired Dual-Stream Network for Gallbladder Disease Diagnosis from Ultrasound
by: Kumar, Vagish, et al.
Published: (2026)

Forecasting N-Body Dynamics: A Comparative Study of Neural Ordinary Differential Equations and Universal Differential Equations
by: S, Suriya R, et al.
Published: (2025)

Competition is the key: A Game Theoretic Causal Discovery Approach
by: Roy, Amartya, et al.
Published: (2025)

TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraints
by: Kumar, Vagish, et al.
Published: (2026)

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
by: White, Alistair, et al.
Published: (2023)

Physics Informed Token Transformer for Solving Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2023)

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
by: Nagy-Huber, Monika, et al.
Published: (2023)

MixFunn: A Neural Network for Differential Equations with Improved Generalization and Interpretability
by: Farias, Tiago de Souza, et al.
Published: (2025)

Graph Neural Ordinary Differential Equations for Coarse-Grained Socioeconomic Dynamics
by: Koch, James, et al.
Published: (2024)

Self-Adaptive Physics-Informed Quantum Machine Learning for Solving Differential Equations
by: Setty, Abhishek, et al.
Published: (2023)

Why DPO is a Misspecified Estimator and How to Fix It
by: Gopalan, Aditya, et al.
Published: (2025)

Generative Discovery of Partial Differential Equations by Learning from Math Handbooks
by: Xu, Hao, et al.
Published: (2025)

What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
by: Mohan, Arvind, et al.
Published: (2024)

Discovering Symbolic Differential Equations with Symmetry Invariants
by: Yang, Jianke, et al.
Published: (2025)

A Posteriori Evaluation of a Physics-Constrained Neural Ordinary Differential Equations Approach Coupled with CFD Solver for Modeling Stiff Chemical Kinetics
by: Kumar, Tadbhagya, et al.
Published: (2023)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
by: Lorsung, Cooper, et al.
Published: (2024)

Robust Physics Discovery from Highly Corrupted Data: A PINN Framework Applied to the Nonlinear Schrödinger Equation
by: Esteves, Pietro de Oliveira
Published: (2026)

A dual physics-informed neural network for topology optimization
by: Singh, Ajendra, et al.
Published: (2024)

Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
by: Song, Sum Kyun, et al.
Published: (2026)

Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
by: Negi, Pawan, et al.
Published: (2024)

DDE-Find: Learning Delay Differential Equations from Noisy, Limited Data
by: Stephany, Robert
Published: (2024)

Quantum Neural Physics: Solving Partial Differential Equations on Quantum Simulators using Quantum Convolutional Neural Networks
by: Zhai, Jucai, et al.
Published: (2026)

Gradient Enhanced Self-Training Physics-Informed Neural Network (gST-PINN) for Solving Nonlinear Partial Differential Equations
by: Iyer, Narayan S, et al.
Published: (2025)

Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation
by: Zhang, Bo, et al.
Published: (2024)

OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration
by: Kantamneni, Subhash, et al.
Published: (2024)

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
by: Gorard, Jonathan, et al.
Published: (2026)

Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
by: Oh, YongKyung, et al.
Published: (2025)

A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design
by: Chakraborty, Atlanta, et al.
Published: (2024)

Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices
by: Guo, Leo, et al.
Published: (2025)

Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent Parametric Partial Differential Equations
by: Hagnberger, Jan, et al.
Published: (2024)

Optimal Data Generation in Multi-Dimensional Parameter Spaces, using Bayesian Optimization
by: Mahani, M. R., et al.
Published: (2023)

Distributed Stochastic Gradient Descent with Staleness: A Stochastic Delay Differential Equation Based Framework
by: Yu, Siyuan, et al.
Published: (2024)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
by: Zheng, Haoyang, et al.
Published: (2025)

Closure Discovery for Coarse-Grained Partial Differential Equations Using Grid-based Reinforcement Learning
by: von Bassewitz, Jan-Philipp, et al.
Published: (2024)