B
Baskar Ganapathysubramanian
Researcher at Iowa State University
Publications - 262
Citations - 6876
Baskar Ganapathysubramanian is an academic researcher from Iowa State University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 34, co-authored 221 publications receiving 4808 citations. Previous affiliations of Baskar Ganapathysubramanian include Cornell University.
Papers
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Journal ArticleDOI
InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models
Ameya Joshi,Minsu Cho,Viraj Shah,Balaji Sesha Sarath Pokuri,Soumik Sarkar,Baskar Ganapathysubramanian,Chinmay Hegde +6 more
TL;DR: A new conditional generative modeling approach (InvNet) is proposed that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties.
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NURBS-based microstructure design for organic photovoltaics
Ramin Noruzi,Sambit Ghadai,Onur Rauf Bingol,Adarsh Krishnamurthy,Baskar Ganapathysubramanian +4 more
TL;DR: This work addresses the efficient design of OSC microstructure by using surface and curve modeling techniques to model the donor–acceptor interface, and use meta-heuristic, gradient-free optimization techniques to optimize the microst structure for maximum short circuit current generation.
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Utilizing morphological correlators for device performance to optimize ternary blend organic solar cells based on block copolymer additives
TL;DR: In this paper, a combination of morphology simulations and device modeling is used to evaluate the performance characteristics of donor-acceptor blend based organic photovoltaics as predicted using device-level simulations.
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Morphology control in polymer blend fibers—a high throughput computing approach
TL;DR: In this article, a high throughput computational approach is used to explore and characterize how processing conditions (specifically blend ratio and evaporation rates) affect the internal morphology of polymer blends during solvent-based fabrication.
Posted Content
A novel graph-based formulation for characterizing morphology with application to organic solar cells
TL;DR: In this paper, a graph-based framework was proposed to compute a broad suite of physically meaningful morphology descriptors for organic solar cells (OSC) and further classified according to the physical subprocesses within OSCs, such as photon absorption, exciton detection, charge separation, and charge transport.