I develop mechanics-informed computational methods for recovering spatially resolved soft-tissue properties from deformation and medical-imaging data.
My work focuses on a central question: how can deformation measurements be converted into reliable, physically interpretable maps of nonlinear tissue mechanics?
Quantifying the information content of loading conditions and selecting deformations that improve multiparameter hyperelastic identifiability.
Explore hyperelastic material characterization →
Recovering voxel-wise nonlinear material parameters from volumetric deformation fields while enforcing equilibrium and boundary reactions.
Explore physics-informed inverse elastography →
Developing interconnected methods for inverse tissue mechanics, deformation-informed imaging, uncertainty-aware characterization, constitutive-model comparison, and experimental translation.
Explore ongoing research →A developing research program linking information-aware experiment design with mechanics-constrained nonlinear inverse characterization.
Recent research and professional milestones.
Recognition for communicating research in computational biomechanics, nonlinear tissue characterization, and physics-informed learning.
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
I am Amirreza “Amir” Asadi (امیررضا اسدی), a PhD candidate in Mechanical Engineering at UC Riverside, working at the intersection of computational mechanics, medical imaging, inverse problems, and machine learning.
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.
Academic training in mechanical engineering, computational mechanics, nonlinear material behavior, and biomechanics.
University of California, Riverside
PhD Candidate working in computational biomechanics, nonlinear tissue mechanics, inverse problems, and physics-informed machine learning.
University of California, Riverside
Graduate training in mechanical engineering with a focus on computational and solid mechanics.
Sharif University of Technology
For research discussions, collaborations, or internship opportunities, reach me through email or the profiles below.
