LabCompass

Adrián Buganza Tepole

Engineering · Purdue University West Lafayette

Mid career · publishing since 2011

Publications

170

Citations

3,621

Est. group size

~19

Recurring co-author estimate

Active years

16

Publishing since 2011

Research summary
AI-generated

This researcher works at the intersection of computational mechanics and machine learning, developing physics-informed neural network models to simulate how biological tissues like skin grow, deform, and heal, as well as how engineered materials deform and fracture. A recurring theme is building 'digital twins' and data-driven material models that combine physical laws with machine learning to predict things like tissue expansion in reconstructive surgery, wound healing, and material damage or fracture. The work spans computational biomechanics, computational materials science, and scientific machine learning methods such as neural ODEs and physics-augmented neural networks.

Physics-informed machine learning for material modelingComputational biomechanics of skin and tissue growthNeural network-based constitutive modelingWound healing and tissue regeneration simulationFracture and damage modeling in solids

Publication output grew steadily from 2017 through a peak around 2023, followed by a slight dip and continued strong activity through 2025-2026.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 19.2/year recently
2017: 5 publications172018: 8 publications182019: 10 publications192020: 12 publications202021: 20 publications212022: 22 publications222023: 27 publications27232024: 16 publications242025: 19 publications252026: 12 publications26
Recent publications
Publishes in
  • arXiv (Cornell University)×21
  • bioRxiv (Cold Spring Harbor Laboratory)×14
  • Acta Biomaterialia×10
  • SSRN Electronic Journal×10
  • Computer Methods in Applied Mechanics and Engineering×8
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This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 20, 2026.

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