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Gabriel S. Gusmão

3 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author2

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.CE2
  • cs.LG1
ORCID 0000-0002-2857-6963

identity via Semantic Scholar / OpenAlex

most citedMaximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems

1 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.CE2024★ 1 cited

Fitting micro-kinetic models to transient kinetics of temporal analysis of product reactors using kinetics-informed neural networks

Dingqi Nai, Gabriel S. Gusmão, Zachary A. Kilwein +2

The temporal analysis of products (TAP) technique produces extensive transient kinetic data sets, but it is challenging to translate the large quantity of raw data into physically…

cs.CE2023

Model-based design of temporal analysis of products (TAP) reactors: A simulated case study in oxidative propane dehydrogenation

Adam C. Yonge, Gabriel S. Gusmão, Rebecca Fushimi +1

Temporal analysis of products (TAP) reactors enable experiments that probe numerous kinetic processes within a single set of experimental data through variations in pulse intensity…

cs.LG2023★ 1 cited

Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems

Gabriel S. Gusmão, Andrew J. Medford

Physics-informed neural networks (PINNs) have proven a suitable mathematical scaffold for solving inverse ordinary (ODE) and partial differential equations (PDE). Typical inverse P…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.