Volumetric untrimming: Precise decomposition of trimmed trivariates into tensor products

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We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

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Volumetric untrimming: Precise decomposition of trimmed trivariates into tensor products

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Convergence analysis of the discretization of continuous-domain inverse problems

Geometric Learning: Leveraging differential geometry for learning and control

FENNECS: a novel particle-in-cell code for simulating the formation of magnetized non-neutral plasmas trapped by electrodes of complex geometries

Anomalous and Chern topological waves in hyperbolic networks

Multimaterial Volumetric Printing of Silica-Based Glasses

Strain-based analysis on the influence of local undercut geometry on fatigue crack initiation life

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Semantic Shape Editing with Parametric Implicit Templates

Optical characterization of a solar concentrating dish system

Stress tolerance of lightweight glass-free PV modules for vehicle integration

Into the ice: Exploration and data capturing in glacial moulins by a tethered robot

Incorporating Projective Geometry into Deep Learning

Cochains are all you need

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Digital Twins of Stone Masonry Buildings for Damage Assessment

3D diffractive optics for linear interconnects and nonlinear processing

T-5 configurations and hyperbolic systems

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Some Physical Implications Of Absolute Geometries In The Description Of Complex Systems Dynamics

Region Extraction in Mesh Intersection