203 related articles for article (PubMed ID: 33913623)
1. Combining data assimilation and machine learning to build data-driven models for unknown long time dynamics-Applications in cardiovascular modeling.
Regazzoni F; Chapelle D; Moireau P
Int J Numer Method Biomed Eng; 2021 Jul; 37(7):e3471. PubMed ID: 33913623
[TBL] [Abstract][Full Text] [Related]
2. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
[TBL] [Abstract][Full Text] [Related]
3. A review of mechanistic learning in mathematical oncology.
Metzcar J; Jutzeler CR; Macklin P; Köhn-Luque A; Brüningk SC
Front Immunol; 2024; 15():1363144. PubMed ID: 38533513
[TBL] [Abstract][Full Text] [Related]
4. Training deep neural density estimators to identify mechanistic models of neural dynamics.
Gonçalves PJ; Lueckmann JM; Deistler M; Nonnenmacher M; Öcal K; Bassetto G; Chintaluri C; Podlaski WF; Haddad SA; Vogels TP; Greenberg DS; Macke JH
Elife; 2020 Sep; 9():. PubMed ID: 32940606
[TBL] [Abstract][Full Text] [Related]
5. Machine-learning-based data-driven discovery of nonlinear phase-field dynamics.
Kiyani E; Silber S; Kooshkbaghi M; Karttunen M
Phys Rev E; 2022 Dec; 106(6-2):065303. PubMed ID: 36671129
[TBL] [Abstract][Full Text] [Related]
6. Multiscale modeling of brain dynamics: from single neurons and networks to mathematical tools.
Siettos C; Starke J
Wiley Interdiscip Rev Syst Biol Med; 2016 Sep; 8(5):438-58. PubMed ID: 27340949
[TBL] [Abstract][Full Text] [Related]
7. The parameter Houlihan: A solution to high-throughput identifiability indeterminacy for brutally ill-posed problems.
Albers DJ; Levine ME; Mamykina L; Hripcsak G
Math Biosci; 2019 Oct; 316():108242. PubMed ID: 31454628
[TBL] [Abstract][Full Text] [Related]
8. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.
Alber M; Buganza Tepole A; Cannon WR; De S; Dura-Bernal S; Garikipati K; Karniadakis G; Lytton WW; Perdikaris P; Petzold L; Kuhl E
NPJ Digit Med; 2019; 2():115. PubMed ID: 31799423
[TBL] [Abstract][Full Text] [Related]
9. Data-Driven Discovery of Mathematical and Physical Relations in Oncology Data Using Human-Understandable Machine Learning.
Kurz D; Sánchez CS; Axenie C
Front Artif Intell; 2021; 4():713690. PubMed ID: 34901835
[TBL] [Abstract][Full Text] [Related]
10. Collocation based training of neural ordinary differential equations.
Roesch E; Rackauckas C; Stumpf MPH
Stat Appl Genet Mol Biol; 2021 Jul; 20(2):37-49. PubMed ID: 34237805
[TBL] [Abstract][Full Text] [Related]
11. Effective models and predictability of chaotic multiscale systems via machine learning.
Borra F; Vulpiani A; Cencini M
Phys Rev E; 2020 Nov; 102(5-1):052203. PubMed ID: 33327059
[TBL] [Abstract][Full Text] [Related]
12. A framework for parameter estimation and model selection in kernel deep stacking networks.
Welchowski T; Schmid M
Artif Intell Med; 2016 Jun; 70():31-40. PubMed ID: 27431035
[TBL] [Abstract][Full Text] [Related]
13. A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: Coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work.
Salman OH; Taha Z; Alsabah MQ; Hussein YS; Mohammed AS; Aal-Nouman M
Comput Methods Programs Biomed; 2021 Sep; 209():106357. PubMed ID: 34438223
[TBL] [Abstract][Full Text] [Related]
14. Machine Learning: Deepest Learning as Statistical Data Assimilation Problems.
Abarbanel HDI; Rozdeba PJ; Shirman S
Neural Comput; 2018 Aug; 30(8):2025-2055. PubMed ID: 29894650
[TBL] [Abstract][Full Text] [Related]
15. Multi-fidelity information fusion with concatenated neural networks.
Pawar S; San O; Vedula P; Rasheed A; Kvamsdal T
Sci Rep; 2022 Apr; 12(1):5900. PubMed ID: 35393511
[TBL] [Abstract][Full Text] [Related]
16. Multiscale modeling meets machine learning: What can we learn?
Peng GCY; Alber M; Tepole AB; Cannon WR; De S; Dura-Bernal S; Garikipati K; Karniadakis G; Lytton WW; Perdikaris P; Petzold L; Kuhl E
Arch Comput Methods Eng; 2021 May; 28(3):1017-1037. PubMed ID: 34093005
[TBL] [Abstract][Full Text] [Related]
17. Reconstructing Genetic Regulatory Networks Using Two-Step Algorithms with the Differential Equation Models of Neural Networks.
Chen CK
Interdiscip Sci; 2018 Dec; 10(4):823-835. PubMed ID: 28748400
[TBL] [Abstract][Full Text] [Related]
18. Emergent Spaces for Coupled Oscillators.
Thiem TN; Kooshkbaghi M; Bertalan T; Laing CR; Kevrekidis IG
Front Comput Neurosci; 2020; 14():36. PubMed ID: 32528268
[TBL] [Abstract][Full Text] [Related]
19. Ensemble-based kernel learning for a class of data assimilation problems with imperfect forward simulators.
Luo X
PLoS One; 2019; 14(7):e0219247. PubMed ID: 31295300
[TBL] [Abstract][Full Text] [Related]
20. Vulnerability of classifiers to evolutionary generated adversarial examples.
Vidnerová P; Neruda R
Neural Netw; 2020 Jul; 127():168-181. PubMed ID: 32361547
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
