01 · Identifiability
Optimal experimental design
Quantifying the information content of loading conditions and selecting deformations that improve multiparameter hyperelastic identifiability.
Paper page →
Amirreza Asadi · PhD Candidate · University of California, Riverside
Computational biomechanics
for nonlinear tissue characterization.
I develop mechanics-informed computational methods for recovering spatially resolved soft-tissue properties from deformation and medical-imaging data.
Research themes
My work focuses on a central question: how can deformation measurements be converted into reliable, physically interpretable maps of nonlinear tissue mechanics?
02 · Inverse imaging
Physics-informed hyperelastic reconstruction
Recovering voxel-wise nonlinear material parameters from volumetric deformation fields while enforcing equilibrium and boundary reactions.
Paper page →03 · Ongoing
Constitutive compatibility atlases
Building noise-aware maps of where different constitutive models are distinguishable, compatible, or observationally equivalent.
Research in progress →Selected publications
A developing research program linking information-aware experiment design with mechanics-constrained nonlinear inverse characterization.
2026
Stress-Constrained Physics-Informed UNet for Voxel-Wise Multiparameter Hyperelastic Inversion in Volumetric Medical Imaging
Amirreza Asadi and Kaveh Laksari · Annals of Biomedical Engineering
2025
Optimal Experimental Design for Repeatable Hyperelastic Material Characterization
Amirreza Asadi and Kaveh Laksari · Journal of the Mechanical Behavior of Biomedical Materials
News
Recent research and professional milestones.
Jul 2026
PI-UNet paper accepted in Annals of Biomedical Engineering.
Earlier 2026
Received the Outstanding Poster Presentation Award
at the Annual Research Symposium organized by the Materials Science
& Mechanical Engineering Graduate Student Association at UC Riverside.
2025
Published work on optimal hyperelastic characterization and experimental design in JMBBM.
Honors & recognition
Recognition for communicating research in computational biomechanics, nonlinear tissue characterization, and physics-informed learning.
Outstanding Poster Presentation Award · 2026
Annual Research Symposium at UC Riverside
Recognized for presenting research on MRI-based voxel-wise nonlinear mechanical characterization of soft tissue using information-aware loading design and physics-informed neural networks.
Materials Science & Mechanical Engineering Graduate Student Association · University of California, Riverside
About
I am a PhD candidate in Mechanical Engineering at UC Riverside, working at the intersection of computational mechanics, medical imaging, inverse problems, and machine learning.
Research direction
My long-term goal is to develop quantitative, mechanics-based imaging methods that move beyond simplified stiffness estimates and recover spatially resolved nonlinear constitutive information.
Institution
University of California, Riverside
Department
Mechanical Engineering
Advisor
Prof. Kaveh Laksari
ORCID
0009-0004-6941-8808
Contact
For research discussions, collaborations, or internship opportunities, reach me through email or the profiles below.