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The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains.
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The stiffness of living tissues and its implications for tissue engineering
Guimarães, C. F., Gasperini, L., Marques, A. P. & Reis, R. L · 2020
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Experimental investigation of the tensile test using digital image correlation (dic) method
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Mechanical characterization and identification of material parameters of porcine aortic valve leaflets
Laville, C., Pradille, C. & Tillier, Y · 2020
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A deep energy method for finite deformation hyperelasticity
Nguyen-Thanh, V. M., Zhuang, X. & Rabczuk, T · 2020
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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
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Systems biology informed deep learning for inferring parameters and hidden dynamics
Yazdani, A., Lu, L., Raissi, M. & Karniadakis, G. E · 2020
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Chang, Z., Zhang, J., Liu, Y., Gao, H. & Xu, G.-K · 2023
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Mechanical behavior and collagen structure of degenerative mitral valve leaflets and a finite element model of primary mitral regurgitation
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Modulation of macrophages by biophysical cues in health and beyond
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The effects of leaflet material properties on the simulated function of regurgitant mitral valves
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A magnetically actuated, optically sensed tensile testing method for mechanical characterization of soft biological tissues
Rosalia, L., Hallou, A., Cochrane, L. & Savin, T · 2023
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Integrated digital image correlation for micro-mechanical parameter identification in multiscale experiments
Rokoš, O., Peerlings, R., Hoefnagels, J. & Geers, M · 2023
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Reliable extrapolation of deep neural operators informed by physics or sparse observations
Zhu, M., Zhang, H., Jiao, A., Karniadakis, G. E. & Lu, L · 2023
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Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics
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Calibrating constitutive models with full-field data via physics informed neural networks
Hamel, C. M., Long, K. N. & Kramer, S. L. B · 2023
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Elasticity imaging using physics-informed neural networks: Spatial discovery of elastic modulus and poisson’s ratio
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Physics‐informed deep‐learning for elasticity: Forward, inverse, and mixed problems
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PINNacle: A comprehensive benchmark of physics-informed neural networks for solving PDEs
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Systems Biology: Identifiability Analysis and Parameter Identification via Systems-Biology-Informed Neural Networks , 87–105 (Springer US, New York, NY, 2023)
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