Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
By Yibo Yang, Paris Perdikaris. 2019
63 citations recorded by the source corpus.
We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input–output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Cynthia Rudin
- Phaedon-Stelios Koutsourelakis
- Constantin Grigo
- Tong Wang
In Statistical and Nonlinear Physics
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Phaedon-Stelios Koutsourelakis
- Cynthia Rudin
- Constantin Grigo
In Statistics and Probability
- Lesia Semenova
- Chudi Zhong
- Zhi Chen
- Haiyang Huang
- Phaedon-Stelios Koutsourelakis
- Cynthia Rudin
- Constantin Grigo
- Berk Ustun
In Probabilistic & Bayesian Methods
- Haiyang Huang
- Phaedon-Stelios Koutsourelakis
- Cynthia Rudin
- Constantin Grigo
- Tong Wang
- Margo I. Seltzer
- Nadia Burkart
- Marco F. Huber
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “…have been used in many scientific applications, including fluid mechanics modeling (Raissi et al., 2020), cardiac activation mapping (Sahli Costabal et al., 2020), stochastic systems modeling (Yang and Perdikaris, 2019), and discovery of differential equations (Raissi et al., 2018; Raissi, 2018).”
- A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime (2019, background): “More recently, deep neural networks (DNNs) LeCun et al. (2015); Goodfellow et al. (2016) have found their way into surrogate modeling of complex computer codes Tripathy and Bilionis (2018); Zhu and Zabaras (2018); Mo et al. (2018); Yang and Perdikaris (2019).”