Open Access

Deep neural network driven simulation based inference method for pole position estimation under model misspecification

Daniel Sadasivan ^1,*, Isaac Cordero^1,†, Andrew Graham^1,‡, Cecilia Marsh ^1,§, Daniel Kupcho^1,∥, Melana Mourad^1,¶, and Maxim Mai ^2,3,**

¹Ave Maria University, Ave Maria, Florida 34142, USA
²Albert Einstein Center for Fundamental Physics, Institute for Theoretical Physics, University of Bern, Sidlerstrasse 5, Bern 3012, Switzerland
³Department of Physics, The George Washington University, 725 21st St, NW, Washington, DC 20052, USA

^*Contact author: daniel.sadasivan@avemaria.edu
^†Contact author: isaac.cordero@my.avemaria.edu
^‡Contact author: andrew.graham@my.avemaria.edu
^§Contact author: cecilia.marsh@my.avemaria.edu
^∥Contact author: daniel.kupcho@my.avemaria.edu
^¶Contact author: melana.mourad@my.avemaria.edu
^**Contact author: maxim.mai@faculty.unibe.ch

PDF

Phys. Rev. D 114, 016002 – Published 6 July, 2026

DOI: https://doi.org/10.1103/ncqd-f1g2

Abstract

The method of simulation based inference is shown to lead to a more accurate resonance parameter estimation than traditional $χ^{2}$ minimization in certain cases of model misspecification in a case-study of $π π$ scattering and the $ρ (770)$ -resonance. Models fit to certain datasets using $χ^{2}$ minimization can make inaccurate predictions for the pole position of the $ρ (770)$ . Simulation based inference (SBI) is shown to make a more robust predictions for the pole positions. This is significant, both as a proof of concept that the SBI method can be used in cases of model misspecification, and because models of $π π$ scattering are a crucial part to many physical systems of contemporary interest [ $a_{1} (1260)$ , $ω (782)$ etc.].

Physics Subject Headings (PhySH)

Article Text

References (102)

Simon Tavaré, David J. Balding, Robert C. Griffiths, and Peter Donnelly, Inferring coalescence times from DNA sequence data, Genetics 145, 505 (1997).
Donald B. Rubin, Bayesianly justifiable, and relevant frequency calculations for the applied statistician, Ann. Stat. 12, 1151 (1984).
Peter J. Diggle and Richard J. Gratton, Monte Carlo methods of inference for implicit statistical models, J. R. Stat. Soc. Ser. B 46, 193 (1984).
Mark A. Beaumont, Wenyang Zhang, and David J. Balding, Approximate Bayesian computation in population genetics, Genetics 162, 2025 (2002).
Johann Brehmer and Kyle Cranmer, Simulation-based inference methods for particle physics, arXiv:2010.06439.
Siddharth Mishra-Sharma, Inferring dark matter substructure with astrometric lensing beyond the power spectrum, Mach. Learn. Sci. Tech. 3, 01LT03 (2022).
Ronan Legin, Yashar Hezaveh, Laurence Perreault Levasseur, and Benjamin Wandelt, Simulation-based inference of strong gravitational lensing parameters, arXiv:2112.05278.
Felipe Amorim, Joey Wisely, Nathan Buckley, Christiana DiNardo, and Daniel Sadasivan, Predicting the Mpemba effect using machine learning, Phys. Rev. E 108, 024137 (2023).
Pedro J. Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F. Podlaski, Sara A. Haddad, Tim P. Vogels, David S. Greenberg, and Jakob H. Macke, Training deep neural density estimators to identify mechanistic models of neural dynamics, eLife 9, e56261 (2020).
Grace Avecilla, Julie Chuong, Fangfei Li, Gavin Sherlock, David Gresham, and Yoav Ram, Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks (2021).
Astro Automata, A tutorial on simulation-based inference (2020), accessed: 2025-05-23.
Patrick Cannon, Daniel Ward, and Sebastian M. Schmon, Investigating the impact of model misspecification in neural simulation-based inference, arXiv:2209.01845.
Noemi Anau Montel, James Alvey, and Christoph Weniger, Tests for model misspecification in simulation-based inference: From local distortions to global model checks, Phys. Rev. D 111, 083013 (2025).
Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi, and Samuel Kaski, Learning robust statistics for simulation-based inference under model misspecification, arXiv:2305.15871.
Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, and Jörn-Henrik Jacobsen, Addressing misspecification in simulation-based inference through data-driven calibration, arXiv:2405.08719.
Maxim Mai, Theory of resonances (2025).
S. D. Protopopescu, M. Alston-Garnjost, A. Barbaro-Galtieri, Stanley M. Flatte, J. H. Friedman, T. A. Lasinski, G. R. Lynch, M. S. Rabin, and F. T. Solmitz, $π π$ Partial wave analysis from reactions $π^{+} p \to π^{+} π^{-} Δ^{+ +}$ and $π^{+} p \to K^{+} K^{-} Δ^{+ +}$ at 7.1-GeV/c, Phys. Rev. D 7, 1279 (1973).
P. Estabrooks and Alan D. Martin, $π π$ Phase shift analysis below the $K \bar{K}$ threshold, Nucl. Phys. B79, 301 (1974).
B. Ananthanarayan, G. Colangelo, J. Gasser, and H. Leutwyler, Roy equation analysis of pi pi scattering, Phys. Rep. 353, 207 (2001).
G. Colangelo, J. Gasser, and H. Leutwyler, $π π$ scattering, Nucl. Phys. B603, 125 (2001).
R. Garcia-Martin, R. Kaminski, J. R. Pelaez, J. Ruiz de Elvira, and F. J. Yndurain, The Pion-pion scattering amplitude. IV: Improved analysis with once subtracted Roy-like equations up to 1100 MeV, Phys. Rev. D 83, 074004 (2011).
Gilberto Colangelo, Martin Hoferichter, and Peter Stoffer, Two-pion contribution to hadronic vacuum polarization, J. High Energy Phys. 02 (2019) 006.
J. R. Pelaez, A. Rodas, and J. Ruiz De Elvira, Global parameterization of $π π$ scattering up to 2 GeV, Eur. Phys. J. C 79, 1008 (2019).
S. Aoki et al. (CS Collaboration), $ρ$ Meson decay in $2 + 1$ flavor lattice QCD, Phys. Rev. D 84, 094505 (2011).
Jozef J. Dudek, Robert G. Edwards, and Christopher E. Thomas (Hadron Spectrum Collaboration), Energy dependence of the $ρ$ resonance in $π π$ elastic scattering from lattice QCD, Phys. Rev. D 87, 034505 (2013); 90, 099902(E) (2014).
Gunnar S. Bali, Sara Collins, Antonio Cox, Gordon Donald, Meinulf Göckeler, C. B. Lang, and Andreas Schäfer (RQCD Collaboration), $ρ$ and $K^{*}$ resonances on the lattice at nearly physical quark masses and $N_{f} = 2$ , Phys. Rev. D 93, 054509 (2016).
David J. Wilson, Raul A. Briceno, Jozef J. Dudek, Robert G. Edwards, and Christopher E. Thomas, Coupled $π π, K \bar{K}$ scattering in $P$ -wave and the $ρ$ resonance from lattice QCD, Phys. Rev. D 92, 094502 (2015).
Ziwen Fu and Lingyun Wang, Studying the $ρ$ resonance parameters with staggered fermions, Phys. Rev. D 94, 034505 (2016).
Constantia Alexandrou, Luka Leskovec, Stefan Meinel, John Negele, Srijit Paul, Marcus Petschlies, Andrew Pochinsky, Gumaro Rendon, and Sergey Syritsyn, $P$ -wave $π π$ scattering and the $ρ$ resonance from lattice QCD, Phys. Rev. D 96, 034525 (2017).
Christian Andersen, John Bulava, Ben Hörz, and Colin Morningstar, The $I = 1$ pion-pion scattering amplitude and timelike pion form factor from $N_{f} = 2 + 1$ lattice QCD, Nucl. Phys. B939, 145 (2019).
Markus Werner et al. (Extended Twisted Mass Collaboration), Hadron-Hadron interactions from $N_{f} = 2 + 1 + 1$ lattice QCD: The $ρ$ -resonance, Eur. Phys. J. A 56, 61 (2020).
Dehua Guo, Andrei Alexandru, Raquel Molina, Maxim Mai, and Michael Döring, Extraction of isoscalar $π π$ phase-shifts from lattice QCD, Phys. Rev. D 98, 014507 (2018).
B. Hu, R. Molina, M. Döring, M. Mai, and A. Alexandru, Chiral extrapolations of the $ρ (770)$ meson in $N_{f} = 2 + 1$ lattice QCD simulations, Phys. Rev. D 96, 034520 (2017).
Bin Hu, Raquel Molina, Michael Doring, Andrei Alexandru, and Dehua Guo, Two-flavor simulations of the $ρ (770)$ and the role of the $K \bar{K}$ channel, Proc. Sci. LATTICE2016 (2016) 096.
Dehua Guo, Andrei Alexandru, Raquel Molina, and Michael Döring, Rho resonance parameters from lattice QCD, Phys. Rev. D 94, 034501 (2016).
Matthias Fischer, Bartosz Kostrzewa, Maxim Mai, Marcus Petschlies, Ferenc Pittler, Martin Ueding, Carsten Urbach, and Markus Werner (Extended Twisted Mass, ETM Collaboration), The $ρ$ -resonance from $N_{f} = 2$ lattice QCD including the physical pion mass, Phys. Lett. B 819, 136449 (2021).
Miguel Salg, Fernando Romero-López, and William I. Jay, Bayesian Analysis and analytic continuation of scattering amplitudes from lattice QCD, Phys. Rev. D 112, 114502 (2025).
D. Sadasivan, M. Mai, and M. Döring, S- and p-wave structure of $S = - 1$ meson-baryon scattering in the resonance region, Phys. Lett. B 789, 329 (2019).
Ferenc Pittler, Maxim Mai, Ulf-G. Meißner, Ryan F. Ferguson, Peter Hurck, David G. Ireland, and Bryan McKinnon, Universal parameters of the $Λ (1380)$ , the $Λ (1405)$ and their isospin partners from a combined analysis of Lattice QCD and experimental results, Phys. Rev. D 112, 074037 (2025).
Maxim Mai, Review of the $Λ (1405)$ A curious case of a strangeness resonance, Eur. Phys. J. Special Topics 230, 1593 (2021).
Maxim Mai, Michael Döring, Carlos Granados, Helmut Haberzettl, Ulf-G. Meißner, Deborah Rönchen, Igor Strakovsky, and Ron Workman (Jülich-Bonn-Washington Collaboration), Jülich-Bonn-Washington model for pion electroproduction multipoles, Phys. Rev. C 103, 065204 (2021).
M. Mai, B. Hu, M. Doring, A. Pilloni, and A. Szczepaniak, Three-body unitarity with isobars revisited, Eur. Phys. J. A 53, 177 (2017).
D. Sadasivan, M. Mai, H. Akdag, and M. Döring, Dalitz plots and lineshape of $a_{1} (1260)$ from a relativistic three-body unitary approach, Phys. Rev. D 101, 094018 (2020); 103, 019901(E) (2021).
Daniel Sadasivan, Andrei Alexandru, Hakan Akdag, Felipe Amorim, Ruairí Brett, Chris Culver, Michael Döring, Frank X. Lee, and Maxim Mai, Pole position of the a1(1260) resonance in a three-body unitary framework, Phys. Rev. D 105, 054020 (2022).
Maxim Mai, Andrei Alexandru, Ruairí Brett, Chris Culver, Michael Döring, Frank X. Lee, and Daniel Sadasivan (GWQCD Collaboration), Three-body dynamics of the A1(1260) resonance from lattice QCD, Phys. Rev. Lett. 127, 222001 (2021).
Yuchuan Feng, Fernando Gil, Michael Döring, Raquel Molina, Maxim Mai, Vanamali Shastry, and Adam Szczepaniak, A unitary coupled-channel three-body amplitude with pions and kaons, Phys. Rev. D 110, 094002 (2024).
Haobo Yan, Maxim Mai, Marco Garofalo, Ulf-G. Meißner, Chuan Liu, Liuming Liu, and Carsten Urbach, $ω$ meson from lattice QCD, Phys. Rev. Lett. 133, 211906 (2024).
Michael Döring, Johann Haidenbauer, Maxim Mai, and Toru Sato, Dynamical coupled-channel models for hadron dynamics, Prog. Part. Nucl. Phys. 146, 104213 (2026).
Marco Garofalo, Maxim Mai, Fernando Romero-López, Akaki Rusetsky, and Carsten Urbach, Three-body resonances in the $φ^{4}$ theory, J. High Energy Phys. 02 (2023) 252.
Maxim Mai, Michael Döring, and Akaki Rusetsky, Multi-particle systems on the lattice and chiral extrapolations: A brief review, Eur. Phys. J. Special Topics 230, 1623 (2021).
Andrei Alexandru, Ruairí Brett, Chris Culver, Michael Döring, Dehua Guo, Frank X. Lee, and Maxim Mai, Finite-volume energy spectrum of the $K^{-} K^{-} K^{-}$ system, Phys. Rev. D 102, 114523 (2020).
Chris Culver, Maxim Mai, Ruairí Brett, Andrei Alexandru, and Michael Döring, Three pion spectrum in the $I = 3$ channel from lattice QCD, Phys. Rev. D 101, 114507 (2020).
M. Mai, M. Döring, C. Culver, and A. Alexandru, Three-body unitarity versus finite-volume $π^{+} π^{+} π^{+}$ spectrum from lattice QCD, Phys. Rev. D 101, 054510 (2020).
Maxim Mai, Chris Culver, Andrei Alexandru, Michael Döring, and Frank X. Lee, Cross-channel study of pion scattering from lattice QCD, Phys. Rev. D 100, 114514 (2019).
C. Culver, M. Mai, A. Alexandru, M. Döring, and F. X. Lee, Pion scattering in the isospin $I = 2$ channel from elongated lattices, Phys. Rev. D 100, 034509 (2019).
Maxim Mai, 3-body quantization condition in unitary isobar formalism, Proc. Sci.LATTICE2018 (2018) 050.
Maxim Mai and Michael Doring, Finite-volume spectrum of $π^{+} π^{+}$ and $π^{+} π^{+} π^{+}$ systems, Phys. Rev. Lett. 122, 062503 (2019).
Michael Döring, H. W. Hammer, Maxim Mai, J. Y. Pang, Akaki Rusetsky, and J. Wu, Three-body spectrum in a finite volume: The role of cubic symmetry, Phys. Rev. D 97, 114508 (2018).
Maxim Mai, Bin Hu, Michael Doring, Alessandro Pilloni, and Adam Szczepaniak, Three-body scattering in isobar ansatz, Proc. Sci. Hadron2017 (2018) 140.
M. Mai and M. Döring, Three-body unitarity in the finite volume, Eur. Phys. J. A 53, 240 (2017).
Peng Guo and Michael Döring, Lattice model of heavy-light three-body system, Phys. Rev. D 101, 034501 (2020).
Peng Guo, Michael Döring, and Adam P. Szczepaniak, Variational approach to $N$ -body interactions in finite volume, Phys. Rev. D 98, 094502 (2018).
M. Mikhasenko et al. (JPAC Collaboration), Dalitz-plot decomposition for three-body decays, Phys. Rev. D 101, 034033 (2020).
M. Albaladejo, D. Winney, I. V. Danilkin, C. Fernández-Ramírez, V. Mathieu, M. Mikhasenko, A. Pilloni, J. A. Silva-Castro, and A. P. Szczepaniak (JPAC Collaboration), Khuri-Treiman equations for $3 π$ decays of particles with spin, Phys. Rev. D 101, 054018 (2020).
M. Mikhasenko, Y. Wunderlich, A. Jackura, V. Mathieu, A. Pilloni, B. Ketzer, and A. P. Szczepaniak, Three-body scattering: Ladders and resonances, J. High Energy Phys. 08 (2019) 080.
L. Ng, L. Bibrzycki, J. Nys, C. Fernandez-Ramirez, A. Pilloni, V. Mathieu, A. J. Rasmusson, and A. P. Szczepaniak (Joint Physics Analysis Center, JPAC Collaboration), Deep learning exotic hadrons, Phys. Rev. D 105, L091501 (2022).
L. Ng, Ł. Bibrzycki, J. Nys, C. Fernández-Ramírez, A. Pilloni, V. Mathieu, A. J. Rasmusson, and A. P. Szczepaniak, Machine learning exotic hadrons, Nuovo Cimento Soc. Ital. Fis. 47C, 207 (2024).
Leonarc Michelle Santos, Vince Angelo A. Chavez, and Denny Lane B. Sombillo, Pole structure of $P ψ N (4312) +$ via machine learning and uniformized S-matrix, J. Phys. G 52, 015104 (2025).
Vince Angelo A. Chavez and Denny Lane B. Sombillo, Line shape analysis of $Λ (1405)$ in $γ p \to K^{+} Σ^{-} π^{+}$ reaction using convolutional neural network, in 21st International Conference on Hadron Spectroscopy and Structure (2025).
M. Malekhosseini, S. Rostami, A. R. Olamaei, R. Ostovar, and K. Azizi, Meson mass and width: Deep learning approach, Phys. Rev. D 110, 054011 (2024).
Darwin Alexander O. Co and Denny Lane B. Sombillo, Analysis of hidden-charm pentaquarks as triangle singularities via deep learning, Proc. Sci. QNP2024 (2025) 031.
Aurélien Dersy, Matthew D. Schwartz, and Alexander Zhiboedov, Reconstructing S-matrix phases with machine learning, J. High Energy Phys. 05 (2024) 200.
Adam B. Mapa and Denny Lane B. Sombillo, Feature extraction in partial wave analysis using $K$ -matrix approach, in 21st International Conference on Hadron Spectroscopy and Structure (2025).
Darwin Alexander O. Co, Vince Angelo A. Chavez, and Denny Lane B. Sombillo, Deep learning framework for disentangling triangle singularity and pole-based enhancements, Phys. Rev. D 110, 114034 (2024).
Denny Lane Sombillo, Extraction of S-matrix pole structure using deep learning, Proc. Sci. PANIC2021 (2022) 175.
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato, and Atsushi Hosaka, Classifying near-threshold enhancement using deep neural network, Few Body Syst. 62, 52 (2021).
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato, and Atsushi Hosaka, Model independent analysis of coupled-channel scattering: A deep learning approach, Phys. Rev. D 104, 036001 (2021).
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato, and Atsushi Hosaka, Unveiling the pole structure of S-matrix using deep learning, Rev. Mex. Fis. Suppl. 3, 0308067 (2022).
Denny Lane B. Sombillo, Yoichi Ikeda, Toru Sato, and Atsushi Hosaka, Classifying the pole of an amplitude using a deep neural network, Phys. Rev. D 102, 016024 (2020).
J. Landay, M. Mai, M. Döring, H. Haberzettl, and K. Nakayama, Towards the minimal spectrum of excited baryons, Phys. Rev. D 99, 016001 (2019).
R. Molina, Justin Landay, Bin Hu, Michael Doring, and Cesar Fernández-Ramírez, Model selection for pion photoproduction, Proc. Sci. Hadron2017 (2018) 135.
Dimitrios Petrellis and Dalibor Skoupil, Ridge regression for minimizing the couplings of hyperon resonances in $K + Λ$ photoproduction, Phys. Rev. C 107, 045206 (2023).
Petr Bydžovský, Aleš Cieplý, Dimitrios Petrellis, Dalibor Skoupil, and Nicholas Zachariou, Model selection for $K + Σ -$ photoproduction within an isobar model, Phys. Rev. C 104, 065202 (2021); 110, 029901(E) (2024).
T. Alghamdi et al., Toward a generative modeling analysis of CLAS exclusive $2 π$ photoproduction, Phys. Rev. D 108, 094030 (2023).
Daniele Binosi, Alessandro Pilloni, and Ralf-Arno Tripolt, Study for a model-independent pole determination of overlapping resonances, Phys. Lett. B 839, 137809 (2023).
Daniel Sadasivan, Maxim Mai, Michael Döring, Ulf-G. Meißner, Felipe Amorim, John Paul Klucik, Jun-Xu Lu, and Li-Sheng Geng, New insights into the pole parameters of the $Λ (1380)$ , the $Λ (1405)$ and the $Σ (1385)$ , Front. Phys. 11, 1139236 (2023).
Feng-Kun Guo, Yuki Kamiya, Maxim Mai, and Ulf-G. Meißner, New insights into the nature of the $Λ (1380)$ and $Λ (1405)$ resonances away from the SU(3) limit, Phys. Lett. B 846, 138264 (2023).
P. C. Bruns, A. Cieplý, and M. Mai, Testing chiral unitary models for the $Λ (1405)$ in $K + π Σ$ photoproduction, Phys. Rev. D 106, 074017 (2022).
Maxim Mai, Status of the $Λ (1405)$ , Few Body Syst. 59, 61 (2018).
Ales Cieplý and Maxim Mai, Theoretical approaches to low energy $\bar{K} N$ interactions, EPJ Web Conf. 130, 02001 (2016).
A. Cieplý, M. Mai, Ulf-G. Meißner, and J. Smejkal, On the pole content of coupled channels chiral approaches used for the $\bar{K} N$ system, Nucl. Phys. A954, 17 (2016).
Maxim Mai and Ulf-G. Meißner, Constraints on the chiral unitary $\bar{K} N$ amplitude from $π Σ K^{+}$ photoproduction data, Eur. Phys. J. A 51, 30 (2015).
Albert Feijoo, Volodymyr Magas, and Angels Ramos, $S = - 1$ meson-baryon interaction and the role of isospin filtering processes, Phys. Rev. C 99, 035211 (2019).
Maxim Mai, Michael Döring, Carlos Granados, Helmut Haberzettl, Jackson Hergenrather, Ulf-G. Meißner, Deborah Rönchen, Igor Strakovsky, and Ron Workman (Jülich-Bonn-Washington Collaboration), Coupled-channels analysis of pion and $η$ electroproduction within the Jülich-Bonn-Washington model, Phys. Rev. C 106, 015201 (2022).
M. Mai, J. Hergenrather, M. Döring, T. Mart, Ulf-G. Meißner, D. Rönchen, and R. Workman (Jülich–Bonn–Washington Collaboration), Inclusion of $K Λ$ electroproduction data in a coupled channel analysis, Eur. Phys. J. A 59, 286 (2023).
R. Navarro Perez, J. E. Amaro, and E. Ruiz Arriola, Statistical error analysis for phenomenological nucleon-nucleon potentials, Phys. Rev. C 89, 064006 (2014).
R. Navarro Pérez, J. E. Amaro, and E. Ruiz Arriola, Coarse-grained potential analysis of neutron-proton and proton-proton scattering below the pion production threshold, Phys. Rev. C 88, 064002 (2013); 91, 029901(E) (2015).
S. Navas et al. (Particle Data Group), Review of particle physics, Phys. Rev. D 110, 030001 (2024).
Martin Hoferichter, Jacobo Ruiz de Elvira, Bastian Kubis, and Ulf-G. Meißner, Nucleon resonance parameters from Roy–Steiner equations, Phys. Lett. B 853, 138698 (2024).
R. Garcia-Martin, R. Kaminski, J. R. Pelaez, and J. Ruiz de Elvira, Precise determination of the f0(600) and f0(980) pole parameters from a dispersive data analysis, Phys. Rev. Lett. 107, 072001 (2011).
J. R. Pelaez, Light scalars as tetraquarks or two-meson states from large N(c) and unitarized chiral perturbation theory, Mod. Phys. Lett. A 19, 2879 (2004).
Joel Grus, Data Science from Scratch: First Principles with python, 2nd ed. (O’Reilly Media, Sebastopol, CA, 2019).

Phys. Rev. D 114, 016002– Published 6 July, 2026

Vol. 114, Iss. 1 — 1 July 2026

Received 19 September 2025
Accepted 16 June 2026

DOI: https://doi.org/10.1103/ncqd-f1g2

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP³.

Published by the American Physical Society

Physical Review D

Reuse & Permissions

Export citation

Export citation

Deep neural network driven simulation based inference method for pole position estimation under model misspecification

Abstract

Physics Subject Headings (PhySH)

Article Text

References (102)

Outline

Information

Filter

Filter

Article Lookup

Enter a citation