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Qijia Zhou

Publications and source records attributed to Qijia Zhou.

3 recordsLinked to original sources

JWST-MIRI's multi-dimensional view of mass loss in the irradiated disks of NGC 1977

The evolution of protoplanetary disks, and consequently the outcomes of planet formation, are thought to be significantly altered in regions containing massive stars. Extreme cases in the Orion Nebula Cluster (ONC) demonstrate the impact of external irradiation (FUV$\gtrsim10^{4}$ G$_{0}$) on disk evolution, but intermediate environments remain less observationally constrained. We present JWST/MIRI Medium Resolution Spectroscopy (MRS) observations of seven proplyds in NGC 1977 exposed to an external FUV field of $10^{3}-10^{5}$ G$_{0}$ from the B1V star 42 Orionis (42 Ori). We characterize emission from molecular (H$_{2}$) and atomic (e.g., [Ne II], [Ar II], HI) species, and in some cases, MIRI reveals extended emission tracing the proplyd ionization front and wind. The closest disk to 42 Ori, KCFF#1, is undergoing extreme mass loss, traced by a 1000s-of-au-long dusty tail, and lacks clear H$_{2}$ or HI emission, indicating an advanced stage of dispersal. The remaining six disks exhibit two-temperature components of H$_{2}$ emission (500--700 K and 1000--1500 K), likely tracing the disk molecular layer and a photoevaporative wind, alongside HI lines which are used to estimate mass accretion rates. When comparing KCFF#2 and #6, which have similar host stars, KCFF#2 (closer to 42 Ori) is dominated by externally driven mass loss, with extended molecular and atomic emission, whereas KCFF#6 only shows extended H$_{2}$ emission, with roughly equal contributions from accretion and external mass loss. While the sample is small, this work demonstrates how JWST/MIRI can assess environmental impacts on disk evolution, with NGC 1977 bridging strongly irradiated disks in the ONC and the more local population.

astro-ph.EP

Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality

Variational inequalities are widely applied in mechanical engineering, fluid penetration, transportation, and other fields. In this paper, a Deep Ritz method based on Physics-Informed Neural Networks (PINNs) is proposed to enhance the accuracy and efficiency of solving elliptic variational inequalities. The Ritz variational method is firstly utilized to transform the variational inequality problem into an optimization problem. Then Bayesian optimization is employed to tune the weights of the loss function, and a residual-based adaptive dataset update strategy is introduced to improve the convergence and accuracy of the model. Numerical experiments show that the proposed method can effectively approximate the analytical solution.

math.NA

Deep Domain Decomposition Method for Solving the Variational Inequality Problems

By integrating physics-informed neural network (PINN) techniques with domain decomposition method, a deep domain decomposition method is presented for solving elliptic variational inequality problems. Based on the Ritz variation method, the elliptic variational inequality problem is firstly reformulated as an optimization problem, and then the subproblem in each subdomain is solved by using the Ritz-PINN method, which the parameters in the network are updated by the Adam optimizer, and the residual-adaptive training by introducing a residual-adaptive dataset update strategy to gradually guide the model to learn more complex regions. Additionally, the impact of overlapping regions on the performance of the new algorithm is explored. Numerical results demonstrate the effectiveness of the proposed algorithm, the mean square error can be reached 1.0e-07, and the number of iterations is independent of grid length h under uniform overlap conditions.

math.NA