Learning Controllable Adaptive Simulation for Multi-resolution Physics

Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learning-based surrogate models use a uniform spatial scale, which needs to resolve to the finest required scale and can waste a huge compute to achieve required accuracy. In this work, we introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We introduce learning techniques that optimizes LAMP with weighted sum of error and computational cost as objective, allowing LAMP to adapt to varying relative importance of error vs. computation tradeoff at inference time. We evaluate our method in a 1D benchmark of nonlinear PDEs and a challenging 2D mesh-based simulation. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error: it achieves an average of 33.7% error reduction for 1D nonlinear PDEs, and outperforms MeshGraphNets + classical Adaptive Mesh Refinement (AMR) in 2D mesh-based simulations. Project website with data and code can be found at: http://snap.stanford.edu/lamp.Comment: ICLR 2023, notable top-25% (spotlight), 19 pages, 9 figure

Iterative precision measurement of branching ratios applied to 5P States in 88Sr+

We report and demonstrate a method for measuring the branching ratios of dipole transitions of trapped atomic ions by performing nested sequences of population inversions. This scheme is broadly applicable to species with metastable lambda systems and can be generalized to find the branching of any state to lowest states. It does not use ultrafast pulsed or narrow linewidth lasers and is insensitive to experimental variables such as laser and magnetic field noise as well as ion heating. To demonstrate its effectiveness, we make the most accurate measurements thus far of the branching ratios of both 5P[subscript 1/2] and 5P[subscript 3/2] states in [superscript 88]Sr[superscript +] with sub-1% uncertainties. We measure 17.175(27) for the 5P[subscript 1/2]–5S[subscript 1/2] branching ratio, 15.845(71) for 5P[subscript 3/2]–5S[subscript 1/2], and 0.056 09(21) for 5P[subscript 3/2]–4D[subscript 5/2]. These values represent the first precision measurement for 5P[subscript 3/2]–4D[subscript 5/2], as well as ten- and thirty-fold improvements in precision respectively for 5P[subscript 1/2]–5S[subscript 1/2] and 5P[subscript 3/2]–5S[subscript 1/2] over the best previous experimental values.National Science Foundation (U.S.). Center for Ultracold AtomsUnited States. Intelligence Advanced Research Projects Activity. Multi-Qubit Coherent Operation

Preventing and Reversing Vacuum-Induced Optical Losses in High-Finesse Tantalum (V) Oxide Mirror Coatings

We study the vacuum-induced degradation of high-finesse optical cavities with mirror coatings composed of SiO$_2$-Ta$_{2}$O$_{5}$ dielectric stacks, and present methods to protect these coatings and to recover their initial quality factor. For separate coatings with reflectivities centered at 370 nm and 422 nm, a vacuum-induced continuous increase in optical loss occurs if the surface-layer coating is made of Ta$_{2}$O$_{5}$, while it does not occur if it is made of SiO$_2$. The incurred optical loss can be reversed by filling the vacuum chamber with oxygen at atmospheric pressure, and the recovery rate can be strongly accelerated by continuous laser illumination at 422 nm. Both the degradation and the recovery processes depend strongly on temperature. We find that a 1 nm-thick layer of SiO$_2$ passivating the Ta$_{2}$O$_{5}$ surface layer is sufficient to reduce the degradation rate by more than a factor of 10, strongly supporting surface oxygen depletion as the primary degradation mechanism.Comment: 14 pages, 7 figure

Turning Erythrocytes into Functional Micromotors

Attempts to apply artificial nano/micromotors for diverse biomedical applications have inspired a variety of strategies for designing motors with diverse propulsion mechanisms and functions. However, existing artificial motors are made exclusively of synthetic materials, which are subject to serious immune attack and clearance upon entering the bloodstream. Herein we report an elegant approach that turns natural red blood cells (RBCs) into functional micromotors with the aid of ultrasound propulsion and magnetic guidance. Iron oxide nanoparticles are loaded into the RBCs, where their asymmetric distribution within the cells results in a net magnetization, thus enabling magnetic alignment and guidance under acoustic propulsion. The RBC motors display efficient guided and prolonged propulsion in various biological fluids, including undiluted whole blood. The stability and functionality of the RBC motors, as well as the tolerability of regular RBCs to the ultrasound operation, are carefully examined. Since the RBC motors preserve the biological and structural features of regular RBCs, these motors possess a wide range of antigenic, transport, and mechanical properties that common synthetic motors cannot achieve and thus hold considerable promise for a number of practical biomedical uses