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Hyungjun Choi

Publications and source records attributed to Hyungjun Choi.

6 recordsLinked to original sources

Self-Similar Solutions of the Two-Dimensional Incompressible Euler Equation from Large Initial Data

Let $0<a<1$ and let $u_0$ be a $C^1$, divergence-free, $(-a)$-homogeneous vector field on $\mathbb{R}^2\setminus\{0\}$. We construct a forward-in-time self-similar solution of the two-dimensional incompressible Euler equations, \[ u(t,x)=t^{-\frac{a}{1+a}} U\left(\frac{x}{t^{{\frac{1}{1+a}}}}\right), \] with initial datum $u_0$. No smallness or sign assumption is imposed on the initial datum. The construction is a vanishing-dissipation limit of hypodissipative self-similar profiles built directly in the critical vorticity space. The main estimate is a uniform critical Lorentz bound $\|\operatorname{curl} U\|_{L^{\frac{2}{1+a},\infty}(\mathbb{R}^2)}$. The resulting Euler solution $u$ belongs to $C([0,\infty);L^2_{\mathrm{loc}}(\mathbb{R}^2))$ and has vorticity uniformly bounded in the critical space $L^{\frac{2}{1+a},\infty}(\mathbb{R}^2)$. Its velocity converges strongly to $u_0$ in $L^2_{\mathrm{loc}}$, while its vorticity converges weak-star to $\omega_0=\operatorname{curl} u_0$ in $L^{\frac{2}{1+a},\infty}$ as $t\downarrow0$.

math.AP

QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs

As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation. We demonstrate that standard "LLM-as-a-Judge" protocols suffer from a systematic Alignment Gap when applied to upper-undergraduate to early graduate level mathematics. To quantify this, we introduce QEDBench, the first large-scale dual-rubric alignment benchmark to systematically measure alignment with human experts on university-level math proofs by contrasting course-specific rubrics against expert common knowledge criteria. By deploying a dual-evaluation matrix (7 judges x 5 solvers) against 1,000+ hours of human evaluation, we reveal that certain frontier evaluators like Claude Opus 4.5, DeepSeek-V3, Qwen 2.5 Max, and Llama 4 Maverick exhibit significant positive bias (up to +0.18, +0.20, +0.30, +0.36 mean score inflation, respectively). Furthermore, we uncover a critical reasoning gap in the discrete domain: while Gemini 3.0 Pro achieves state-of-the-art performance (0.91 average human evaluation score), other reasoning models like GPT-5 Pro and Claude Sonnet 4.5 see their performance significantly degrade in discrete domains. Specifically, their average human evaluation scores drop to 0.72 and 0.63 in Discrete Math, and to 0.74 and 0.50 in Graph Theory. In addition to these research results, we also release QEDBench as a public benchmark for evaluating and improving AI judges. Our benchmark is publicly published at https://github.com/qqliu/Yale-QEDBench.

cs.LG

Multi-Sink Solutions to the Self-Similar Euler Equations

We construct examples and provide a classification of self-similar solutions to the two-dimensional incompressible Euler equations whose pseudo-velocity fields possess more than one stagnation point. These solutions are also homogeneous steady states of the Euler equations. In contrast, we prove that any homogeneous self-similar solution with bounded vorticity away from the origin necessarily admits only a single stagnation point, located at the origin. The solutions we construct develop velocity cusps along rays from the origin, and this allows for additional stagnation points of the pseudo-velocity field.

math.AP

Asymmetric Self-similar Spiral Solutions of 2-D Incomressible Euler Equations

We construct nonradial, self-similar solutions to the two-dimensional incompressible Euler equations without assuming rotational symmetry. These solutions extend the study of self-similar algebraic spiral flows, initiated by Elling and further developed by Shao-Wei-Zhang [41], where m-fold symmetry with m>=2 was assumed. Moreover, they bear resemblance to the numerical simulations of Bressan-Shen [10], in connection with the ongoing investigation into non-uniqueness of solutions.

math.AP

Global well-posedness of slightly supercritical SQG equations and gradient estimate

We prove the global regularity of smooth solutions for a dissipative surface quasi-geostrophic equation with both velocity and dissipation logarithmically supercritical compared to the critical equation. By this, we mean that a symbol defined as a power of logarithm is added to both velocity and dissipation terms to penalize the equation's criticality. Our primary tool is the nonlinear maximum principle which provides transparent proofs of global regularity for nonlinear dissipative equations. Combining the nonlinear maximum principle with a modulus of continuity, we prove a uniform-in-time gradient estimate for the critical and slightly supercritical surface quasi-geostrophic equation. It improves the previous double exponential bound by Kiselev-Nazarov-Volberg to the single exponential. In addition, we prove eventual exponential decay of the solutions.

math.AP

Emergent behaviors of discrete Lohe aggregation flows

The Lohe sphere model and the Lohe matrix model are prototype continuous aggregation models on the unit sphere and the unitary group, respectively. These models have been extensively investigated in recent literature. In this paper, we propose several discrete counterparts for the continuous Lohe type aggregation models and study their emergent behaviors using the Lyapunov function method. For suitable discretization of the Lohe sphere model, we employ a scheme consisting of two steps. In the first step, we solve the first-order forward Euler scheme, and in the second step, we project the intermediate state onto the unit sphere. For this discrete model, we present a sufficient framework leading to the complete state aggregation in terms of system parameters and initial data. For the discretization of the Lohe matrix model, we use the Lie group integrator method, Lie-Trotter splitting method and Strang splitting method to propose three discrete models. For these models, we also provide several analytical frameworks leading to complete state aggregation and asymptotic state-locking.

math-ph