Zobrazeno 1 - 10
of 165
pro vyhledávání: '"Schäfer, Frank P."'
Machine learning the Ising transition: A comparison between discriminative and generative approaches
The detection of phase transitions is a central task in many-body physics. To automate this process, the task can be phrased as a classification problem. Classification problems can be approached in two fundamentally distinct ways: through either a d
Externí odkaz:
http://arxiv.org/abs/2411.19370
Autor:
Sapienza, Facundo, Bolibar, Jordi, Schäfer, Frank, Groenke, Brian, Pal, Avik, Boussange, Victor, Heimbach, Patrick, Hooker, Giles, Pérez, Fernando, Persson, Per-Olof, Rackauckas, Christopher
The differentiable programming paradigm is a cornerstone of modern scientific computing. It refers to numerical methods for computing the gradient of a numerical model's output. Many scientific models are based on differential equations, where differ
Externí odkaz:
http://arxiv.org/abs/2406.09699
In a physical system, changing parameters such as temperature can induce a phase transition: an abrupt change from one state of matter to another. Analogous phenomena have recently been observed in large language models. Typically, the task of identi
Externí odkaz:
http://arxiv.org/abs/2405.17088
Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here, we explain the inner workings and identify potential failure modes o
Externí odkaz:
http://arxiv.org/abs/2311.10710
Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the underlying phases is the learning-by-confusion scheme. As input, it req
Externí odkaz:
http://arxiv.org/abs/2311.09128
Publikováno v:
Phys. Rev. Lett. 132, 207301 (2024)
One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classific
Externí odkaz:
http://arxiv.org/abs/2306.14894
Autor:
Arya, Gaurav, Seyer, Ruben, Schäfer, Frank, Chandra, Kartik, Lew, Alexander K., Huot, Mathieu, Mansinghka, Vikash K., Ragan-Kelley, Jonathan, Rackauckas, Christopher, Schauer, Moritz
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it. Our approach fuses recent advances in stochastic
Externí odkaz:
http://arxiv.org/abs/2306.07961
Publikováno v:
IEEE Transactions on Automatic Control, 69(11), 8057-8063, 2024
The limits of quantum feedback control have immediate consequences for quantum information science at large, yet remain largely unexplored. Here, we combine quantum filtering theory and moment-sum-of-squares techniques to construct a hierarchy of con
Externí odkaz:
http://arxiv.org/abs/2304.03366
Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradien
Externí odkaz:
http://arxiv.org/abs/2210.08572
The spectral conductivity, i.e., the electrical conductivity as a function of the Fermi energy, is a cornerstone in determining the thermoelectric transport properties of electrons. However, the spectral conductivity depends on sample-specific proper
Externí odkaz:
http://arxiv.org/abs/2206.01100