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Xiaoxuan Zhang

8 papers hereh-index 15644 citations27 works total

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

author position
  • first author4
  • middle author3

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CE2
  • cond-mat.mtrl-sci1
  • math.NA1
  • physics.app-ph1
  • physics.bio-ph1
  • q-bio.PE1
same name
  • Xiaoxuan Zhang — 2 papers, h 6
  • Xiaoxuan Zhang — 1 paper
  • Xiaoxuan Zhang — 1 paper
  • Xiaoxuan Zhang — 1 paper, h 2
  • Xiaoxuan Zhang — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202023
most citedMachine learning materials physics: Multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures

79 citations · 117 across the 7 of their papers we have counts for

collaborators
Showing 2021Show all

3 papers · 1 filter

physics.bio-ph2021

System inference via field inversion for the spatio-temporal progression of infectious diseases: Studies of COVID-19 in Michigan and Mexico

Zhenlin Wang, Mariana Carrasco Teja, Xiaoxuan Zhang +2

We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) ver…

q-bio.QM2021★ 16 cited

Biomembranes undergo complex, non-axisymmetric deformations governed by Kirchhoff-Love kinematics and revealed by a three dimensional computational framework

Debabrata Auddya, Xiaoxuan Zhang, Rahul Gulati +4

Biomembranes play a central role in various phenomena like locomotion of cells, cell-cell interactions, packaging of nutrients, and in maintaining organelle morphology and function…

cs.CE2021★ 17 cited

Bayesian neural networks for weak solution of PDEs with uncertainty quantification

Xiaoxuan Zhang, Krishna Garikipati

Solving partial differential equations (PDEs) is the canonical approach for understanding the behavior of physical systems. However, large scale solutions of PDEs using state of th…

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