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Found 7 papers, 2 papers with code
Heterogeneous Peridynamic Neural Operators: Discover Biotissue Constitutive Law and Microstructure From Digital Image Correlation Measurements
Then, in the second phase we reinitialize the learnt bond force and the kernel function, and training them together with a fiber orientation field for each material point.
MetaNO: How to Transfer Your Knowledge on Learning Hidden Physics
Gradient-based meta-learning methods have primarily been applied to classical machine learning tasks such as image classification.
Anomaly Detection in Driving by Cluster Analysis Twice
Thus, detecting anomalies in driving is critical for the T&L industry.
A Bayesian constitutive model selection framework for biaxial mechanical testing of planar soft tissues: application to porcine aortic valves
Although the model that best fits the experimental data can be deemed the most suitable model, this practice often can be insufficient given the inter-sample variability of experimental observations.
A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements
To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge.
Cryopreservation of seeds of blue waterlily (Nymphaea caerulea) using glutathione adding plant vitrification solution, PVS+
The only way to long-term store N. caerulea seeds is cryopreservation.
Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling
In this work, we propose to use data-driven modeling, which directly utilizes high-fidelity simulation and/or experimental measurements to predict a material's response without using conventional constitutive models.
