From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems
The numerical sign problem poses a seemingly insurmountable barrier to the simulation of many fascinating systems. We apply neural networks to deform the region of integration, mitigating the sign problem of systems with strongly correlated electrons. In this talk we present our latest architectural...
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| creator | Rodekamp, Marcel Berkowitz, Evan Dincă, Maria Gäntgen, Christoph Krieg, Stefan Luu, Thomas |
| description | The numerical sign problem poses a seemingly insurmountable barrier to the
simulation of many fascinating systems. We apply neural networks to deform the
region of integration, mitigating the sign problem of systems with strongly
correlated electrons. In this talk we present our latest architectural
developments as applied to contour deformation. We also demonstrate its
applicability to real systems, namely perylene. |
| doi_str_mv | 10.48550/arxiv.2311.18312 |
| format | Article |
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region of integration, mitigating the sign problem of systems with strongly
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region of integration, mitigating the sign problem of systems with strongly
correlated electrons. In this talk we present our latest architectural
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simulation of many fascinating systems. We apply neural networks to deform the
region of integration, mitigating the sign problem of systems with strongly
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| subjects | Physics - High Energy Physics - Lattice Physics - Strongly Correlated Electrons |
| title | From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems |
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