Qingqing Zhao (赵青青)
My research interests lie at the intersection of Science and Machine Learning, aiming to advance both fields. The research objective is twofold:
1.) MLaccelerated simulation with Physics guidance: incorporate scientific knowledge into ML algorithms for improved performance and robustness
2.) MLpowered workflow for design and structure discovery: develop MLpowered solutions for accelerated scientific simulations, diverse and superior design generation, and accurate structural discovery


Deep Born Operator Learning for Reflection Tomographic Imaging
Qingqing Zhao, Yanting Ma, Petros T. Boufounos, Saleh Nabi, Hassan Mansour
ICASSP 2023
abstract 
Recent developments in wavebased sensor technologies, such as ground penetrating radar (GPR), provide new opportunities for accurate imaging of underground scenes. Given measurements of the scattered electromagnetic wavefield, the goal is to estimate the spatial distribution of the permittivity of the underground scenes. However, such problems are highly illposed, difficult to formulate, and computationally expensive. In this paper, we propose a physicsinspired machine learningbased method to learn the wavematter interaction under the GPR setting. The learned forward model is combined with a learned signal prior to recover the permittivity distribution of the unknown underground scenes. We test our approach on a dataset of 400 permittivity maps with a threelayer background, which is challenging to solve using existing methods. We demonstrate via numerical simulation that our method achieves a 50% improvement in mean squared error over the benchmark machine learningbased solvers for reconstructing layered underground scenes.
@inproceedings{BornGPR,
title={Deep Born Operator Learning for Reflection Tomographic Imaging},
author={Qingqing Zhao*, Yanting Ma, Petros T. Boufounos,
Saleh Nabi, Hassan Mansour}
journal={under_review},
year={2022}
}


Learning Controllable Adaptive Simulation for Multiresolution Physics
Tailin Wu*, Takashi Maruyama*, Qingqing Zhao*, Gordon Wetzstein, Jure Leskovec
ICLR 2023, Spotlight
webpage 
OpenReview 
abstract 
bibtex 
Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multiscale dynamics: a small fraction of the system is extremely dynamic, and requires very finegrained resolution, while a majority of the system is changing slowly and can be modeled by coarser spatial scales. Typical learningbased 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 Multiscale Physics (LAMP) as the first full deep learningbased 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 GNNbased actorcritic 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, which allows LAMP to adapt to varying relative importance of error vs. computation tradeoff at inference time. We test our method in a 1D benchmark of nonlinear PDEs and a challenging 2D meshbased simulation. We demonstrate that our LAMP outperforms stateoftheart deep learning surrogate models with up to 60.5\% error reduction, and is able to adaptively tradeoff computation to improve longterm prediction error.
@inproceedings{Tailingraphpde,
title={Learning Controllable Adaptive Simulation
for Multiscale Physics},
author={Tailin Wu, Takashi Maruyama,
Qingqing Zhao, Gordon Wetzstein,
Jure Leskovec}
journal={ICLR},
year={2023}
}


Learning to Solve PDEconstrained Inverse Problems with Graph Networks
Qingqing Zhao, David B. Lindell, Gordon Wetzstein
ICML 2022
webpage 
abstract 
bibtex 
github 
video
Learned graph neural networks (GNNs) have recently been established as fast and accurate alternatives for principled solvers in simulating the dynamics of physical systems. In many application domains across science and engineering, however, we are not only interested in a forward simulation but also in solving inverse problems with constraints defined by a partial differential equation (PDE). Here we explore GNNs to solve such PDEconstrained inverse problems. Given a sparse set of measurements, we are interested in recovering the initial condition or parameters of the PDE. We demonstrate that GNNs combined with autodecoderstyle priors are wellsuited for these tasks, achieving more accurate estimates of initial conditions or physical parameters than other learned approaches when applied to the wave equation or NavierStokes equations. We also demonstrate computational speedups of up to 90x using GNNs compared to principled solvers.
@inproceedings{qzhao2022graphpde,
title={Learning to Solve PDEconstrained
Inverse Problems
with Graph Networks},
author={Qingqing Zhao and David B. Lindell
and Gordon Wetzstein}
journal={ICML},
year={2022}
}


Minimum DielectricResonator Mode Volumes
Qingqing Zhao, Lang Zhang, Owen D. Miller
pdf 
abstract 
We show that global lower bounds to the mode volume of a dielectric resonator can be computed via Lagrangian duality. Stateoftheart designs rely on sharp tips, but such structures appear to be highly suboptimal at nanometerscale feature sizes, and we demonstrate that computational inverse design offers ordersofmagnitude possible improvements. Our bound can be applied for geometries that are simultaneously resonant at multiple frequencies, for highefficiency nonlinearoptics applications, and we identify the unavoidable penalties that must accompany such multiresonant structures.
@misc{qzhaomodev,
url = {https://arxiv.org/abs/2008.13241},
author = {Qingqing Zhao and Lang Zhang and Owen D. Miller},
title = {Minimum DielectricResonator Mode Volumes},
publisher = {arXiv},
year = {2020},
}


Large Isospin Asymmetry in 22Si/22O Mirror GamowTeller Transitions
Reveals the Halo Structure of 22Al
J. Lee, et. al. (RIBLL Collaboration)
Physical Review Letters, 2020
pdf 
abstract 
βdelayed oneproton emissions of 22Si, the lightest nucleus with an isospin projection Tz ¼ −3, are studied with a silicon array surrounded by highpurity germanium detectors. Properties of βdecay branches and the reduced transition probabilities for the transitions to the lowlying states of 22Al are determined. Compared to the mirror β decay of 22O, the largest value of mirror asymmetry in lowlying states by far, with δ ¼ 209ð96Þ, is found in the transition to the first 1þ excited state. Shellmodel calculation with isospinnonconserving forces, including the T ¼ 1, J ¼ 2, 3 interaction related to the s1=2 orbit that introduces explicitly the isospinsymmetry breaking force and describes the loosely bound nature of the wave functions of the s1=2 orbit, can reproduce the observed data well and consistently explain the observation that a large δ value occurs for the first but not for the second 1þ excited state of 22Al. Our results, while supporting the protonhalo structure in 22Al, might provide another means to identify halo nuclei.

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