US 12,437,194 B2
Mechanistic model parameter inference through artificial intelligence
Viatcheslav Gurev, Bedford Hills, NY (US); James R. Kozloski, New Fairfield, CT (US); Kenney Ng, Arlington, MA (US); and Jaimit Parikh, Danbury, CT (US)
Assigned to INTERNATIONAL BUSINESS MACHINES CORPORATION, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Jun. 28, 2021, as Appl. No. 17/360,666.
Prior Publication US 2022/0414452 A1, Dec. 29, 2022
Int. Cl. G06N 3/08 (2023.01); G06N 3/045 (2023.01); G06N 3/094 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/045 (2023.01); G06N 3/094 (2023.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a memory that stores computer executable components; and
a processor that executes at least one of the computer executable components that:
constructs a machine learning architecture comprising:
an encoder layer of the machine learning architecture, wherein the encoder layer comprises at least one encoder and at least one machine learning network, and wherein the at least one machine learning network comprises at least one generative adversarial network;
an intermediate layer of the machine learning architecture that is communicatively coupled to the encoder layer, wherein the intermediate layer comprise at least one bijector node; and
a decoder layer of the machine learning architecture that is communicatively coupled to the intermediate layer, wherein the decoder layer comprises a mechanistic model that operates as a decoder of the machine learning architecture, and wherein the at least one bijector node transforms a Gaussian distribution generated by the at least one machine learning network to a prior distribution of model parameters of the mechanistic model inputted to the at least one machine learning network; and
trains the machine learning architecture to identify a causal relationship in the mechanistic model using a parameter space of the mechanistic model as a learned distribution sampled within the at least one generative adversarial network based on a sample subset of outputs of the mechanistic model.