Zobrazeno 1 - 10
of 1 409
pro vyhledávání: '"Van Vaerenbergh, P."'
We establish universality of the renormalised energy for mappings from a planar domain to a compact manifold, by approximating subquadratic polar convex functionals of the form $\int_\Omega f(|\mathrm{D} u|)\,\mathrm{d} x$. The analysis relies on the
Externí odkaz:
http://arxiv.org/abs/2411.17520
Autor:
Marchesin, Federico, Hejda, Matěj, Carmona, Tzamn Melendez, Di Carlo, Stefano, Savino, Alessandro, Pavanello, Fabio, Van Vaerenbergh, Thomas, Bienstman, Peter
Matrix-vector multiplications (MVMs) are essential for a wide range of applications, particularly in modern machine learning and quantum computing. In photonics, there is growing interest in developing architectures capable of performing linear opera
Externí odkaz:
http://arxiv.org/abs/2411.02243
Autor:
Zhang, Xiangyi, Böhm, Fabian, Valiante, Elisabetta, Noori, Moslem, Van Vaerenbergh, Thomas, Yang, Chan-Woo, Pedretti, Giacomo, Mohseni, Masoud, Beausoleil, Raymond, Rozada, Ignacio
In-memory computing (IMC) has been shown to be a promising approach for solving binary optimization problems while significantly reducing energy and latency. Building on the advantages of parallel computation, we propose an IMC-compatible parallelism
Externí odkaz:
http://arxiv.org/abs/2409.09152
Autor:
Vermani, Ayesha, Dowling, Matthew, Jeon, Hyungju, Jordan, Ian, Nassar, Josue, Bernaerts, Yves, Zhao, Yuan, Van Vaerenbergh, Steven, Park, Il Memming
Function and dysfunctions of neural systems are tied to the temporal evolution of neural states. The current limitations in showing their causal role stem largely from the absence of tools capable of probing the brain's internal state in real-time. T
Externí odkaz:
http://arxiv.org/abs/2409.01280
Autor:
Peng, Yiwei, Hooten, Sean, Yu, Xinling, Van Vaerenbergh, Thomas, Yuan, Yuan, Xiao, Xian, Tossoun, Bassem, Cheung, Stanley, Fiorentino, Marco, Beausoleil, Raymond
Kolmogorov-Arnold Networks (KAN) models were recently proposed and claimed to provide improved parameter scaling and interpretability compared to conventional multilayer perceptron (MLP) models. Inspired by the KAN architecture, we propose the Photon
Externí odkaz:
http://arxiv.org/abs/2408.08407
Autor:
Yu, Xinling, Hooten, Sean, Liu, Ziyue, Zhao, Yequan, Fiorentino, Marco, Van Vaerenbergh, Thomas, Zhang, Zheng
Operator learning has become a powerful tool in machine learning for modeling complex physical systems governed by partial differential equations (PDEs). Although Deep Operator Networks (DeepONet) show promise, they require extensive data acquisition
Externí odkaz:
http://arxiv.org/abs/2407.11253
Autor:
Hejda, Matěj, Marchesin, Federico, Papadimitriou, George, Gizopoulos, Dimitris, Charbonnier, Benoit, Orobtchouk, Régis, Bienstman, Peter, Van Vaerenbergh, Thomas, Pavanello, Fabio
In this work, we discuss our vision for neuromorphic accelerators based on integrated photonics within the framework of the Horizon Europe NEUROPULS project. Augmented integrated photonic architectures that leverage phase-change and III-V materials f
Externí odkaz:
http://arxiv.org/abs/2407.06240
Autor:
Bhattacharya, T., Hutchinson, G. H., Pedretti, G., Sheng, X., Ignowski, J., Van Vaerenbergh, T., Beausoleil, R., Strachan, J. P., Strukov, D. B.
Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms, e.g., based on gradient descent or conjugate gradient methods that are at the core of control, machine learning, and o
Externí odkaz:
http://arxiv.org/abs/2401.16204
We study the limiting behavior of minimizing $p$-harmonic maps from a bounded Lipschitz domain $\Omega \subset \mathbb{R}^{3}$ to a compact connected Riemannian manifold without boundary and with finite fundamental group as $p \nearrow 2$. We prove t
Externí odkaz:
http://arxiv.org/abs/2401.03583
Autor:
Hooten, Sean, Sun, Peng, Gantz, Liron, Fiorentino, Marco, Beausoleil, Raymond G., Van Vaerenbergh, Thomas
Shape optimization approaches to inverse design offer low-dimensional, physically-guided parameterizations of structures by representing them as combinations of shape primitives. However, on discretized rectilinear simulation grids, computing the gra
Externí odkaz:
http://arxiv.org/abs/2311.05646