Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences-Reference-Cited by-同舟云学术

Explainable and trustworthy artificial intelligence for correctable modeling in chemical sciences

Author:

Feng Jinchao¹^ORCID,Lansford Joshua L.²^ORCID,Katsoulakis Markos A.³^ORCID,Vlachos Dionisios G.²⁴^ORCID

Affiliation:

1. Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA.

2. Department of Chemical and Biomolecular Engineering, University of Delaware,150 Academy Street, Colburn Laboratory Newark, DE 19716, USA.

3. Department of Mathematics and Statistics, University of Massachusetts at Amherst, Amherst, MA 01003, USA.

4. Catalysis Center for Energy Innovation, University of Delaware, 221 Academy Street, 250R, Newark, DE 19716, USA.

Abstract

The developed framework apportions model error to inputs, computes predictive guarantees, and enables model correctability.

Funder

National Science Foundation

U.S. Department of Energy

Air Force Office of Scientific Research

Defense Advanced Research Projects Agency

Blue Waters Fellowship

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference141 articles.

1. Molecular dynamics simulations: Advances and applications;Hospital A.;Adv. Appl. Bioinforma. Chem.,2015

5. S. Skogestad I. Postlethwaite Multivariable Feedback Control: Analysis and Design (Wiley New York 2007) vol. 2.

Cited by 31 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献