Re-entrant magic-angle phenomena in twisted bilayer graphene in integer magnetic fluxes

In this work we address the re-entrance of magic-angle phenomena (band flatness and quantum-geometric transport) in twisted bilayer graphene (TBG) subjected to strong magnetic fluxes $\pm \Phi_0$, $\pm 2 \Phi_0$, $\pm 3 \Phi_0$... ($\Phi_0 = h/e$ is the flux quantum per moir\'e cell). The moir\'e translation invariance is restored at the integer fluxes, for which we calculate the TBG band structure using accurate atomistic models with lattice relaxations. Similarly to the zero-flux physics outside the magic angle condition, the reported effect breaks down rapidly with the twist. We conclude that the magic-angle physics re-emerges in high magnetic fields, witnessed by the appearance of flat electronic bands distinct from Landau levels, and manifesting non-trivial quantum geometry. We further discuss the possible flat-band quantum geometric contribution to the superfluid weight in strong magnetic fields (28 T at 1.08$^\circ$ twist), according to Peotta-T\"{o}rm\"{a} mechanism.Comment: 5 pages, 5 figure

Learning physics-constrained subgrid-scale closures in the small-data regime for stable and accurate LES

We demonstrate how incorporating physics constraints into convolutional neural networks (CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy simulations (LES) in the small-data regime (i.e., when the availability of high-quality training data is limited). Using several setups of forced 2D turbulence as the testbeds, we examine the {\it a priori} and {\it a posteriori} performance of three methods for incorporating physics: 1) data augmentation (DA), 2) CNN with group convolutions (GCNN), and 3) loss functions that enforce a global enstrophy-transfer conservation (EnsCon). While the data-driven closures from physics-agnostic CNNs trained in the big-data regime are accurate and stable, and outperform dynamic Smagorinsky (DSMAG) closures, their performance substantially deteriorate when these CNNs are trained with 40x fewer samples (the small-data regime). We show that CNN with DA and GCNN address this issue and each produce accurate and stable data-driven closures in the small-data regime. Despite its simplicity, DA, which adds appropriately rotated samples to the training set, performs as well or in some cases even better than GCNN, which uses a sophisticated equivariance-preserving architecture. EnsCon, which combines structural modeling with aspect of functional modeling, also produces accurate and stable closures in the small-data regime. Overall, GCNN+EnCon, which combines these two physics constraints, shows the best {\it a posteriori} performance in this regime. These results illustrate the power of physics-constrained learning in the small-data regime for accurate and stable LES.Comment: 23 pages, 9 figure