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Parameter Estimation with Dense and Convolutional Neural Networks
  Applied to the FitzHugh-Nagumo ODE
v1v2v3 (latest)

Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE

Mathematical and Scientific Machine Learning (MSML), 2020
12 December 2020
J. Rudi
J. Bessac
Amanda Lenzi
ArXiv (abs)PDFHTML

Papers citing "Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE"

19 / 19 papers shown
Machine learning approach to single-shot multiparameter estimation for the non-linear Schrödinger equation
Machine learning approach to single-shot multiparameter estimation for the non-linear Schrödinger equation
Louis Rossignol
Tangui Aladjidi
Myrann Baker-Rasooli
Quentin Glorieux
118
0
0
23 Sep 2025
Interpretable neural network system identification method for two families of second-order systems based on characteristic curves
Interpretable neural network system identification method for two families of second-order systems based on characteristic curves
Federico J. Gonzalez
Luis P. Lara
185
1
0
12 Sep 2025
NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models
NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models
Luca Ghafourpour
Valentin Duruisseaux
Bahareh Tolooshams
Philip H. Wong
Costas A. Anastassiou
A. Anandkumar
455
4
0
05 Jun 2025
Fast Likelihood-Free Parameter Estimation for Lévy Processes
Fast Likelihood-Free Parameter Estimation for Lévy Processes
Nicolas Coloma
William Kleiber
381
1
0
03 May 2025
PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
PINNverse: Accurate parameter estimation in differential equations from noisy data with constrained physics-informed neural networks
Marius Almanstötter
Roman Vetter
Dagmar Iber
PINN
369
4
0
07 Apr 2025
Estimating unknown parameters in differential equations with a
  reinforcement learning based PSO method
Estimating unknown parameters in differential equations with a reinforcement learning based PSO method
Wenkui Sun
Xiaoya Fan
Lijuan Jia
Tinyi Chu
Shing-Tung Yau
Rongling Wu
Zhong Wang
231
1
0
13 Nov 2024
When are dynamical systems learned from time series data statistically accurate?
When are dynamical systems learned from time series data statistically accurate?Neural Information Processing Systems (NeurIPS), 2024
Jeongjin Park
Nicole Yang
Nisha Chandramoorthy
AI4TS
355
11
0
09 Nov 2024
A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal
  Experimental Design
A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design
Atlanta Chakraborty
Xun Huan
Tommie A. Catanach
307
6
0
18 Aug 2024
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems
Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems
Rui Zhang
O. Chkrebtii
Dongbin Xiu
277
1
0
19 Jul 2024
Latent Variable Sequence Identification for Cognitive Models with Neural
  Bayes Estimation
Latent Variable Sequence Identification for Cognitive Models with Neural Bayes Estimation
Ti-Fen Pan
Jing-Jing Li
Bill Thompson
Anne Collins
BDL
608
0
0
20 Jun 2024
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks
Matthew Sainsbury-Dale
A. Zammit‐Mangion
J. Richards
Raphael Huser
1.1K
36
0
04 Oct 2023
Statistical treatment of convolutional neural network super-resolution
  of inland surface wind for subgrid-scale variability quantification
Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantificationArtificial Intelligence for the Earth Systems (AIES), 2022
Daniel J. Getter
J. Bessac
J. Rudi
Yan Feng
248
2
0
30 Nov 2022
Physically constrained neural networks to solve the inverse problem for
  neuron models
Physically constrained neural networks to solve the inverse problem for neuron models
Matteo Ferrante
A. Duggento
N. Toschi
PINNAI4CE
156
1
0
24 Sep 2022
Likelihood-Free Parameter Estimation with Neural Bayes Estimators
Likelihood-Free Parameter Estimation with Neural Bayes EstimatorsAmerican Statistician (Am. Stat.), 2022
Matthew Sainsbury-Dale
A. Zammit‐Mangion
Raphael Huser
669
63
0
27 Aug 2022
Statistical Deep Learning for Spatial and Spatio-Temporal Data
Statistical Deep Learning for Spatial and Spatio-Temporal DataAnnual Review of Statistics and Its Application (ARSIA), 2022
C. Wikle
A. Zammit‐Mangion
BDL
344
68
0
05 Jun 2022
Learning to Estimate Without Bias
Learning to Estimate Without BiasIEEE Transactions on Signal Processing (IEEE TSP), 2021
Niv Nayman
Rong Jin
Lihi Zelnik-Manor
353
15
0
24 Oct 2021
SWAT Watershed Model Calibration using Deep Learning
SWAT Watershed Model Calibration using Deep Learning
M. Mudunuru
K. Son
Pin Jiang
X. Chen
230
4
0
06 Oct 2021
Neural Networks for Parameter Estimation in Intractable Models
Neural Networks for Parameter Estimation in Intractable ModelsComputational Statistics & Data Analysis (CSDA), 2021
Amanda Lenzi
J. Bessac
J. Rudi
Michael L. Stein
BDL
599
73
0
29 Jul 2021
A regime switching on Covid19 analysis and prediction in Romania
A regime switching on Covid19 analysis and prediction in RomaniaScientific Reports (Sci Rep), 2020
Marian Petrica
Radu D. Stochitoiu
Marius Leordeanu
Ionel Popescu
186
4
0
27 Jul 2020
1
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