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Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning. [PDF]
Zhu S+7 more
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Iterative Learning Control Without Resetting Conditions of an Algorithm Based on a Finite-Time Zeroing Neural Network. [PDF]
Chai Y+5 more
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Gross-Pitaevskii systems of fractional order with respect to multicomponent solitary wave dynamics. [PDF]
Bilal M+6 more
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Differentiable signed distance function rendering
ACM Transactions on Graphics, 2022Physically-based differentiable rendering has recently emerged as an attractive new technique for solving inverse problems that recover complete 3D scene representations from images.
Delio Vicini+2 more
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DFR: Differentiable Function Rendering for Learning 3D Generation from Images
Computer graphics forum (Print), 2020Learning‐based 3D generation is a popular research field in computer graphics. Recently, some works adapted implicit function defined by a neural network to represent 3D objects and have become the current state‐of‐the‐art.
Yunjie Wu, Zhengxing Sun
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Boletim da Sociedade Brasileira de Matemática, 1980
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2016
We can now begin the rigorous treatment of calculus in earnest, starting with the notion of a derivative. We can now define derivatives analytically, using limits, in contrast to the geometric definition of derivatives, which uses tangents. The advantage of working analytically is that (a) we do not need to know the axioms of geometry, and (b) these ...
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We can now begin the rigorous treatment of calculus in earnest, starting with the notion of a derivative. We can now define derivatives analytically, using limits, in contrast to the geometric definition of derivatives, which uses tangents. The advantage of working analytically is that (a) we do not need to know the axioms of geometry, and (b) these ...
openaire +2 more sources