Zobrazeno 1 - 10
of 7 637
pro vyhledávání: '"Glatt A"'
We study a class of semi-linear differential Volterra equations with polynomial-type potentials that incorporates the effects of memory while being subjected to random perturbations via an additive Gaussian noise. We show that for a broad class of no
Externí odkaz:
http://arxiv.org/abs/2411.02459
Autor:
Glatt-Holtz, Nathan E., Holbrook, Andrew J., Krometis, Justin A., Mondaini, Cecilia F., Sheth, Ami
In the first edition of this Handbook, two remarkable chapters consider seemingly distinct yet deeply connected subjects ...
Comment: To appear in the Handbook of MCMC, 2nd Edition
Comment: To appear in the Handbook of MCMC, 2nd Edition
Externí odkaz:
http://arxiv.org/abs/2410.17398
In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the original model ac
Externí odkaz:
http://arxiv.org/abs/2407.00356
In this work, we explore the use of deep learning techniques to learn the relationships between nuclear cross-sections across the chart of isotopes. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime that are
Externí odkaz:
http://arxiv.org/abs/2404.02332
Autor:
Weber, Matthias, Grießer, Andreas, Mosbach, Dennis, Glatt, Erik, Wiegmann, Andreas, Schmidt, Volker
Quantifying the relationship between geometric descriptors of microstructure and effective properties like permeability is essential for understanding and improving the behavior of porous materials. In this paper, we employ a previously developed sto
Externí odkaz:
http://arxiv.org/abs/2311.13944
Multi-objective Quantum Annealing approach for solving flexible job shop scheduling in manufacturing
Publikováno v:
Journal of Manufacturing Systems Volume 72, February 2024, Pages 142-153
Flexible Job Shop Scheduling (FJSSP) is a complex optimization problem crucial for real-world process scheduling in manufacturing. Efficiently solving such problems is vital for maintaining competitiveness. This paper introduces Quantum Annealing-bas
Externí odkaz:
http://arxiv.org/abs/2311.09637
Autor:
Glatt, Ruben, Liu, Shusen
Emerging foundation models in machine learning are models trained on vast amounts of data that have been shown to generalize well to new tasks. Often these models can be prompted with multi-modal inputs that range from natural language descriptions o
Externí odkaz:
http://arxiv.org/abs/2306.17400
Autor:
Didier, Gustavo, Glatt-Holtz, Nathan E., Holbrook, Andrew J., Magee, Andrew F., Suchard, Marc A.
The continuous-time Markov chain (CTMC) is the mathematical workhorse of evolutionary biology. Learning CTMC model parameters using modern, gradient-based methods requires the derivative of the matrix exponential evaluated at the CTMC's infinitesimal
Externí odkaz:
http://arxiv.org/abs/2306.15841
Autor:
de Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., Ingelse, L., Fonseca, A., Landajuela, M., Petersen, B., Glatt, R., Mundhenk, N., Lee, C. S., Hochhalter, J. D., Randall, D. L., Kamienny, P., Zhang, H., Dick, G., Simon, A., Burlacu, B., Kasak, Jaan, Machado, Meera, Wilstrup, Casper, La Cava, W. G.
Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model that can be insightful to users. Historically, the majority of algorithms
Externí odkaz:
http://arxiv.org/abs/2304.01117
Autor:
Naumenko, Denys, Burian, Max, Marmiroli, Benedetta, Haider, Richard, Radeticchio, Andrea, Wagner, Lucas, Piazza, Luca, Glatt, Lisa, Brandstetter, Stefan, Zilio, Simone Dal, Biasiol, Giorgio, Amenitsch, Heinz
Understanding and control of thermal transport in solids at nanoscale is crucial to engineer and to enhance properties of a new generation of optoelectronic, thermoelectric, and photonic devices. In this regard, semiconductor superlattice structures
Externí odkaz:
http://arxiv.org/abs/2302.11972