Bayesian Neural Ordinary Differential Equations

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Bayesian Neural Ordinary Differential Equations

14 December 2020

Aslan Garcia-Valadez

Krishna Vishal Vemula

Chris Rackauckas

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Papers citing "Bayesian Neural Ordinary Differential Equations"

13 / 13 papers shown

Title
Uncertainty quantification of neural network models of evolving processes via Langevin sampling Cosmin Safta Reese E. Jones Ravi G. Patel Raelynn Wonnacot Dan S. Bolintineanu Craig M. Hamel S. Kramer BDL 124 0 0 21 Apr 2025
Uncertainty and Structure in Neural Ordinary Differential Equations Katharina Ott Michael Tiemann Philipp Hennig AI4CE 116 5 0 22 May 2023
Interpretable Polynomial Neural Ordinary Differential Equations Colby Fronk Linda R. Petzold 82 27 0 09 Aug 2022
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs Çağatay Yıldız M. Kandemir Barbara Rakitsch 131 12 0 24 May 2022
Predicting the impact of treatments over time with uncertainty aware neural differential equations E. Brouwer J. Hernández Stephanie L. Hyland OOD CML 55 26 0 24 Feb 2022
Latent Time Neural Ordinary Differential Equations Srinivas Anumasa P. K. Srijith BDL 41 5 0 23 Dec 2021
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities Indranil Pan L. Mason Omar K. Matar AI4CE 102 46 0 07 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 63 98 0 02 Nov 2021
Beyond Predictions in Neural ODEs: Identification and Interventions H. Aliee Fabian J. Theis Niki Kilbertus CML 118 25 0 23 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 88 16 0 21 Jun 2021
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling Björn Lütjens Catherine H. Crawford Mark S. Veillette Dava Newman 82 10 0 05 May 2021
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data Mohamed Aziz Bhouri P. Perdikaris 81 21 0 04 Mar 2021
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations Winnie Xu Ricky T. Q. Chen Xuechen Li David Duvenaud BDL UQCV 92 49 0 12 Feb 2021