Samuel Frank
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Samuel Frank has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- James Halverson
Northeastern University
3 shared publications - Anindita Maiti
Perimeter Institute for Theoretical Physics, Waterloo, Ontario N2L 2Y5, Canada
2 shared publications - Fabian Ruehle
Northeastern University
2 shared publications A. Koeder
Abteilung Halbleiterphysik, Universität Ulm, D-89069 Ulm, Germany
1 shared publication- Andreas Waag
Institute of Semiconductor Technology, Technische Universität Braunschweig 1 , Hans-Sommer-Str. 66, 38106 Braunschweig
1 shared publication - Christian Ferko
Northeastern University
1 shared publication
Latest Publications
- Open Access
- 14 October 2026
- Published by Springer Nature in Journal of High Energy Physics
- Vol. 2026 (10) , 143
Anomalies in neural network field theory
A bstract: Neural network field theory (NN-FT) formulates field theory in terms of a network architecture and a density on its parameters. We derive Schwinger–Dyson equations and Ward identities in NN-FT and utilize them to study anomalies. The equations depend on a conserved parameter space current that characterizes symmetries and how they break. It is relevant even in non-local NN-FTs, but can recover local currents in the case of a local Lagrangian by an appropriate fiber-wise average. In machine learning, this formalism is applied to feedforward networks and the attention mechanism. In physics, we use this machinery to study U(1) symmetry for a complex scalar, the scale anomaly in 4 d massless ϕ 4 theory, the Weyl anomaly for the bosonic string (including a new computation of the critical dimension), and examples involving discrete topological data, such as winding numbers and T-duality. Since the results are obtained in network parameter space rather than the standard field space, they represent a new way to understand symmetries in quantum field theories.
- 14 October 2026
- Published by Springer Nature in Journal of High Energy Physics
- Vol. 2026 (10) , 143
Open AccessAnomalies in neural network field theory
A bstract: Neural network field theory (NN-FT) formulates field theory in terms of a network architecture and a density on its parameters. We derive Schwinger–Dyson equations and Ward identities in NN-FT and utilize them to study anomalies. The equations depend on a conserved parameter space current that characterizes symmetries and how they break. It is relevant even in non-local NN-FTs, but can recover local currents in the case of a local Lagrangian by an appropriate fiber-wise average. In machine learning, this formalism is applied to feedforward networks and the attention mechanism. In physics, we use this machinery to study U(1) symmetry for a complex scalar, the scale anomaly in 4 d massless ϕ 4 theory, the Weyl anomaly for the bosonic string (including a new computation of the critical dimension), and examples involving discrete topological data, such as winding numbers and T-duality. Since the results are obtained in network parameter space rather than the standard field space, they represent a new way to understand symmetries in quantum field theories.
- Open Access
- 1 August 2026
- Published by IOP Publishing in Machine Learning: Science and Technology
- Vol. 7 (4) , 045070
Fermions and supersymmetry in neural network field theories
We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.
- 1 August 2026
- Published by IOP Publishing in Machine Learning: Science and Technology
- Vol. 7 (4) , 045070
Open AccessFermions and supersymmetry in neural network field theories
We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.
- Open Access
Fermions and Supersymmetry in Neural Network Field Theories
We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.
Open AccessFermions and Supersymmetry in Neural Network Field Theories
We introduce fermionic neural network field theories via Grassmann-valued neural networks. Free theories are obtained by a generalization of the Central Limit Theorem to Grassmann variables. This enables the realization of the free Dirac spinor at infinite width and a four fermion interaction at finite width. Yukawa couplings are introduced by breaking the statistical independence of the output weights for the fermionic and bosonic fields. A large class of interacting supersymmetric quantum mechanics and field theory models are introduced by super-affine transformations on the input that realize a superspace formalism.
- Open Access
- 8 December 2023
- Published by American Physical Society (APS) in Physical Review Research
- Vol. 5 (4) , 043218
Cluster tomography in percolation
In cluster tomography, we propose measuring the number of clusters intersected by a line segment of length across a finite sample. As expected, the leading order of scales as , where depends on microscopic details of the system. However, at criticality, there is often an additional nonlinearity of the form , originating from the endpoints of the line segment. By performing large scale Monte Carlo simulations of both two- and three-dimensional percolation, we find that is universal and depends only on the angles encountered at the endpoints of the line segment intersecting the sample. Our findings are further supported by analytic arguments in two dimensions, building on results in conformal field theory. Being broadly applicable, cluster tomography can be an efficient tool for detecting phase transitions and characterizing the corresponding universality class in classical or quantum systems with a relevant cluster structure.- 8 December 2023
- Published by American Physical Society (APS) in Physical Review Research
- Vol. 5 (4) , 043218
Open AccessCluster tomography in percolation
In cluster tomography, we propose measuring the number of clusters intersected by a line segment of length across a finite sample. As expected, the leading order of scales as , where depends on microscopic details of the system. However, at criticality, there is often an additional nonlinearity of the form , originating from the endpoints of the line segment. By performing large scale Monte Carlo simulations of both two- and three-dimensional percolation, we find that is universal and depends only on the angles encountered at the endpoints of the line segment intersecting the sample. Our findings are further supported by analytic arguments in two dimensions, building on results in conformal field theory. Being broadly applicable, cluster tomography can be an efficient tool for detecting phase transitions and characterizing the corresponding universality class in classical or quantum systems with a relevant cluster structure. - Open Access
- 31 May 2005
- Published by American Physical Society (APS) in Physical Review B
- Vol. 71 (20) , 205213
Effect of annealing on the depth profile of hole concentration in (Ga,Mn)As
The effect of annealing at on the carrier depth profile, Mn distribution, electrical conductivity, and Curie temperature of (Ga,Mn)As layers with thicknesses , grown by molecular-beam epitaxy at low temperatures, is studied by a variety of analytical methods. The vertical gradient in hole concentration, revealed by electrochemical capacitance-voltage profiling, is shown to play a key role in the understanding of conductivity and magnetization data. The gradient, basically already present in as-grown samples, is strongly influenced by post-growth annealing. From secondary ion mass spectroscopy it can be concluded that, at least in thick layers, the change in carrier depth profile and thus in conductivity is not primarily due to out-diffusion of Mn interstitials during annealing. Two alternative possible models are discussed.- 31 May 2005
- Published by American Physical Society (APS) in Physical Review B
- Vol. 71 (20) , 205213
Open AccessEffect of annealing on the depth profile of hole concentration in (Ga,Mn)As
The effect of annealing at on the carrier depth profile, Mn distribution, electrical conductivity, and Curie temperature of (Ga,Mn)As layers with thicknesses , grown by molecular-beam epitaxy at low temperatures, is studied by a variety of analytical methods. The vertical gradient in hole concentration, revealed by electrochemical capacitance-voltage profiling, is shown to play a key role in the understanding of conductivity and magnetization data. The gradient, basically already present in as-grown samples, is strongly influenced by post-growth annealing. From secondary ion mass spectroscopy it can be concluded that, at least in thick layers, the change in carrier depth profile and thus in conductivity is not primarily due to out-diffusion of Mn interstitials during annealing. Two alternative possible models are discussed.