Towards an interpretable machine learning model for electrospun polyvinylidene fluoride (PVDF) fiber properties

Shrutidhara Sarma; Akarshit Kumar Verma; Saket Sanjay Phadkule; Manabendra Saharia

doi:10.1016/j.commatsci.2022.111661

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Towards an interpretable machine learning model for electrospun polyvinylidene fluoride (PVDF) fiber properties

Shrutidhara Sarma Akarshit Kumar Verma Saket Sanjay Phadkule Manabendra Saharia

Year: 2022 Journal: Computational Materials Science Vol: 213 Pages: 111661-111661 Publisher: Elsevier BV

DOI: 10.1016/j.commatsci.2022.111661

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Keywords:

Polyvinylidene fluoride Bridging (networking) Electrospinning Materials science Polymer Computer science Fiber Experimental data Approximation error Process (computing) Machine learning Artificial intelligence Biological system Mechanical engineering Composite material Algorithm Mathematics Engineering

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Advanced Sensor and Energy Harvesting Materials

Physical Sciences → Engineering → Biomedical Engineering

Electrospun Nanofibers in Biomedical Applications

Physical Sciences → Materials Science → Biomaterials

Conducting polymers and applications

Physical Sciences → Materials Science → Polymers and Plastics

Towards an interpretable machine learning model for electrospun polyvinylidene fluoride (PVDF) fiber properties

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