arXiv subjects

Jian Deng

Publications and source records attributed to Jian Deng.

At least 19 recordsLinked to original sources

arXiv2026

The Dynamical Instability of Rotating Boson Stars

We investigate the dynamical instability of rotating boson stars described by the Gross--Pitaevskii--Poisson equations with contact self-interactions. Through three-dimensional simulations, we confirm that the rotating boson star undergoes a quasiperiodic conversion between ring-like and twin-star-like density configurations in the early nonlinear stage. We develop a systematic linear stability analysis to identify the modes driving this instability and show that repulsive self-interactions could significantly increase the lifetime of the rotating boson stars. We further construct a three-mode Hamiltonian to describe the early nonlinear stage, which explains the quasiperiodic conversion. This analytical framework agrees reasonably well with the simulation results and provides a clear picture to understand the dynamics of rotating boson stars.

gr-qc↗

arXiv2026

Autonomous Optimization of Complex Oxides for Thermochemical Fuel Production

Two-step thermochemical fuel production, including H2O and CO2 splitting, offers a promising route to sustainable fuel manufacturing, with performance governed by redox-active oxides that enable cyclic reduction-oxidation reactions. Maximizing thermal-to-fuel conversion efficiency demands materials that simultaneously satisfy multiple stringent thermodynamic and kinetic targets. Addressing these requirements has increasingly driven materials design toward complex, multi-cation oxides, such as mixed-cation fluorites, perovskites, and high-entropy oxides, wherein composition, defect chemistry, phase stability, and morphology should be co-optimized. This creates a challenging materials optimization problem that is poorly suited to traditional trial-and-error approaches. In this review, we argue that thermochemical fuel production provides a compelling frontier for autonomous materials design and optimization. We first examine why redox-active complex oxides are difficult to develop, owing to multidimensional phase spaces, harsh operating conditions, and competing functional targets. We then discuss how high-throughput computation, automated synthesis, characterization and testing, and machine learning can be integrated into closed-loop workflows to address these challenges. Building on broader oxide materials research, we organize recent progress into a capability roadmap for complex-oxide optimization, spanning compositionally diverse synthesis, operando characterization, robotic testing, operation-condition computation, and multi-objective optimization. Finally, we outline key experimental, computational, and data challenges for building self-improving materials development platforms for materials development in thermochemical fuel production.

cond-mat.mtrl-sci↗

arXiv2026

Mechanism-Dependent Descriptors Enable Predictive Design of Oxygen Capacity in Perovskite Oxides

Perovskite oxides can reversibly accommodate substantial changes in oxygen stoichiometry, making them attractive for clean-energy technologies including chemical looping and oxygen storage. Despite extensive efforts to optimize their redox properties, predictive descriptors capable of assessing oxygen capacity across diverse compositions remain under development. Here, we combine experiments and first-principles calculations to establish composition and oxygen-capacity relationships in the model perovskite series LnxSr1-xCoO3. We confirm that increasing Sr2+ content promotes the formation of high-valence Co4+, expanding the cationic redox reservoir available during oxygen release and thereby enhancing oxygen capacity. In this regime, oxygen-vacancy formation energy captures the observed trend because oxygen release is primarily compensated by Co4+/Co3+/Co2+ redox. Across the rare-earth series, however, oxygen capacity decreases from La to Lu despite progressively lower oxygen-vacancy formation energies. We reveal that this counterintuitive behavior originates from an alternative charge-compensation pathway, in which lattice oxygen is partially oxidized to O1- -like species during oxygen removal. Heavy rare-earth compositions (Tb-Lu) preferentially stabilize these oxygen-hole species through distinct local bonding environments, with charge compensation involving both oxidized lattice oxygen and reduced rare-earth and cobalt cations, thereby suppressing net oxygen release despite favorable vacancy thermodynamics. We further identify average metal-oxygen bond strength, quantified by integrated crystal orbital Hamilton population, as a physically meaningful descriptor for oxygen capacity when anionic redox becomes dominant.

cond-mat.mtrl-sci↗

arXiv2026

An Adjoint-Based Differentiable Physics Framework for Online Parameter Inversion in Closed-Brayton Gas-Cooled Reactor Digital Twins

Digital twins for advanced reactors must invert physical parameters online from noisy, partial sensor streams while the plant is rarely at steady state. Gradient-based inversion is the natural tool for this task, but the forward models in routine use are seldom differentiable end to end, so practitioners fall back on derivative-free filters. We present an end-to-end-differentiable digital twin of a closed-Brayton gas-cooled reactor that propagates reverse-mode automatic differentiation through an implicit differential-algebraic plant model, exposing exact parameter sensitivities. The twin drives an AD-Hessian incremental 4D-Var estimator, benchmarked against ensemble, unscented, and finite-difference variational baselines over a two-by-two matrix that crosses steady with transient excitation and full with partial observation. No single estimator wins everywhere: the unscented filter keeps its best-linear-unbiased advantage on the controlled steady-state corner, while the proposed estimator attains the lowest mean error on the reflector coefficient on the other three corners --- reaching \SI{0.43}{\percent} under transient full observation, roughly an order of magnitude below the unscented filter, and matching the ensemble filter at low-to-moderate noise on the combined transient-partial corner. Every estimator's variance sits within a small factor of the Cram\'er--Rao bound, so the residual error reflects a deterministic bias floor from the twin's differentiability simplifications rather than statistical inefficiency, and the advantage is one of robustness to the resulting multi-modal loss landscape, which a component ablation localises by regime. Differentiating a first-principles plant model thus makes gradient-based inversion competitive with established filters across a reactor's operating range.

eess.SY↗

arXiv2026

jaxdae: A JAX-native Differentiable Solver for Differential-Algebraic Equations in Coupled Multi-physics

Many engineered models begin as partial differential equations. Spatial discretization converts them into ordinary differential equations coupled to algebraic constraints---conservation closures, constitutive laws, network topology---whose joint evolution is a differential-algebraic equation (DAE). Parameter inversion, uncertainty quantification, Bayesian inference, and optimal control all require gradients of this solve. The two software traditions that should supply them have not met: industrial acausal modeling tools simulate DAEs forward but stop at reverse-mode differentiation, while differentiable-physics frameworks in JAX handle explicit ODEs and PDEs but leave the algebraic-constraint layer untouched. Here we show that the forward DAE solve and its reverse-mode sensitivity can be unified in one JAX-native suite. jaxdae pairs adaptive BDF, Radau, and Rosenbrock integration with Pantelides index reduction and dummy derivatives, and makes the adaptive BDF path differentiable by freezing the accepted step grid and re-solving a variable-step BDF-2 on it for the backward pass. The full pipeline differentiates under one $\texttt{jax.grad}$ call, XLA fuses a batched parameter sweep into one program whose wall time stays nearly flat from batch~1 to~1000, and the DAE becomes a differentiable primitive for inference, control, and design.

cs.MS↗

arXiv2025

Eq.Bot: Enhance Robotic Manipulation Learning via Group Equivariant Canonicalization

Robotic manipulation systems are increasingly deployed across diverse domains. Yet existing multi-modal learning frameworks lack inherent guarantees of geometric consistency, struggling to handle spatial transformations such as rotations and translations. While recent works attempt to introduce equivariance through bespoke architectural modifications, these methods suffer from high implementation complexity, computational cost, and poor portability. Inspired by human cognitive processes in spatial reasoning, we propose Eq.Bot, a universal canonicalization framework grounded in SE(2) group equivariant theory for robotic manipulation learning. Our framework transforms observations into a canonical space, applies an existing policy, and maps the resulting actions back to the original space. As a model-agnostic solution, Eq.Bot aims to endow models with spatial equivariance without requiring architectural modifications. Extensive experiments demonstrate the superiority of Eq.Bot under both CNN-based (e.g., CLIPort) and Transformer-based (e.g., OpenVLA-OFT) architectures over existing methods on various robotic manipulation tasks, where the most significant improvement can reach 50.0%.

cs.RO↗

arXiv2025

Physics-informed neural networks for hidden boundary detection and flow field reconstruction

Simultaneously detecting hidden solid boundaries and reconstructing flow fields from sparse observations poses a significant inverse challenge in fluid mechanics. This study presents a physics-informed neural network (PINN) framework designed to infer the presence, shape, and motion of static or moving solid boundaries within a flow field. By integrating a body fraction parameter into the governing equations, the model enforces no-slip/no-penetration boundary conditions in solid regions while preserving conservation laws of fluid dynamics. Using partial flow field data, the method simultaneously reconstructs the unknown flow field and infers the body fraction distribution, thereby revealing solid boundaries. The framework is validated across diverse scenarios, including incompressible Navier-Stokes and compressible Euler flows, such as steady flow past a fixed cylinder, an inline oscillating cylinder, and subsonic flow over an airfoil. The results demonstrate accurate detection of hidden boundaries, reconstruction of missing flow data, and estimation of trajectories and velocities of a moving body. Further analysis examines the effects of data sparsity, velocity-only measurements, and noise on inference accuracy. The proposed method exhibits robustness and versatility, highlighting its potential for applications when only limited experimental or numerical data are available.

physics.flu-dyn↗

arXiv2024

Optimization and Simulation of Startup Control for Space Nuclear Power Systems with Closed Brayton Cycle based on NuHeXSys

This paper presents the development and optimization of a Space Nuclear Power System (SNPS) utilizing a helium-xenon gas-cooled Closed Brayton Cycle (CBC). A comprehensive dynamic system analysis code NuHeXSys (Nuclear Helium-Xenon Brayton Cycle Power System) was created, integrating non-ideal gas properties, a multi-channel thermal-hydraulic reactor core, and detailed turbo-machinery components. The innovation lies in parametrization of startup control sequence and application of an evolutionary algorithm (NSGA-II) to improve control performance, significantly reducing startup time and energy consumption. Model verification shows parameter deviations within 10%, confirming its accuracy. The optimized control strategy reduced startup time by 1260 seconds and lowered external energy demand by 17%, demonstrating improved efficiency and operational stability for deep space missions. This work provides a foundation for future advancements in optimizing space nuclear power systems.

eess.SY↗

arXiv2024

Data-driven modeling of unsteady flow based on deep operator network

Time-dependent flow fields are typically generated by a computational fluid dynamics (CFD) method, which is an extremely time-consuming process. However, the latent relationship between the flow fields is governed by the Navier-Stokes equations and can be described by an operator. We therefore train a deep operator network, or simply DeepONet, to learn the temporal evolution between flow snapshots. Once properly trained, given a few consecutive snapshots as input, the network has a great potential to generate the next snapshot accurately and quickly. Using the output as a new input, the network iterates the process, generating a series of successive snapshots with little wall time. Specifically, we consider 2D flow around a circular cylinder at Reynolds number 1000, and prepare a set of high-fidelity data using a high-order spectral/hp element method as ground truth. Although the flow fields are periodic, there are many small-scale features in the wake flow that are difficult to generate accurately. Furthermore, any discrepancy between the prediction and the ground truth for the first snapshots can easily accumulate during the iterative process, which eventually amplifies the overall deviations. Therefore, we propose two alternative techniques to improve the training of DeepONet. The first one enhances the feature extraction of the network by harnessing the "multi-head non-local block". The second one refines the network parameters by leveraging the local smooth optimization technique. Both techniques prove to be highly effective in reducing the cumulative errors and our results outperform those of the dynamic mode decomposition method.

physics.flu-dyn↗

arXiv2023

Physics-informed neural networks for unsteady incompressible flows with time-dependent moving boundaries

Physics-informed neural networks (PINNs) employed in fluid mechanics deal primarily with stationary boundaries. This hinders the capability to address a wide range of flow problems involving moving bodies. To this end, we propose a novel extension, which enables PINNs to solve incompressible flows with time-dependent moving boundaries. More specifically, we impose Dirichlet constraints of velocity at the moving interfaces and define new loss functions for the corresponding training points. Moreover, we refine training points for flows around the moving boundaries for accuracy. This effectively enforces the no-slip condition of the moving boundaries. With an initial condition, the extended PINNs solve unsteady flow problems with time-dependent moving boundaries and still have the flexibility to leverage partial data to reconstruct the entire flow field. Therefore, the extended version inherits the amalgamation of both physics and data from the original PINNs. With a series of typical flow problems, we demonstrate the effectiveness and accuracy of the extended PINNs. The proposed concept allows for solving inverse problems as well, which calls for further investigations.

physics.flu-dyn↗

arXiv2023

Evolution of global polarization in relativistic heavy-ion collisions within a perturbative approach

Extremely large angular orbital momentum can be produced in non-central heavy-ion collisions, leading to a strong transverse polarization of partons that scatter through the quark-gluon plasma (QGP) due to spin-orbital coupling. We develop a perturbative approach to describe the formation and spacetime evolution of quark polarization inside the QGP. Polarization from both the initial hard scatterings and interactions with the QGP have been consistently described using the quark-potential scattering approach, which has been coupled to realistic initial condition calculation and the subsequent (3+1)-dimensional viscous hydrodynamic simulation of the QGP for the first time. Within this improved approach, we have found that different spacetime-rapidity-dependent initial energy density distributions generate different time evolution profiles of the longitudinal flow velocity gradient of the QGP, which further lead to an approximately 15% difference in the final polarization of quarks collected on the hadronization hypersurface of the QGP. Therefore, in addition to the collective flow coefficients, the hyperon polarization may serve as a novel tool to help constrain the initial condition of the hot nuclear matter created in high-energy nuclear collisions.

nucl-th↗

arXiv2023

Bouncing behaviour of a particle settling through a density transition layer

The present work focuses on a specific bouncing behaviour as a particle settling through a three-layer stratified fluid in the absence of neutral buoyant position, which was firstly discovered by Abaid, N., Adalsteinsson D., Agyapong A. & McLaughlin, R.M. (2004) in salinity-induced stratification. Both experiments and numerical simulations are carried out. In our experiments, illuminated by a laser sheet on the central plane of the particle, its bouncing behaviour is well captured. We find that the bouncing process starts after the wake detaches from the particle. The PIV results show that an upward jet is generated at the central axis behind the particle after the wake breaks. By conducting a force decomposition procedure, we quantify the enhanced drag caused by the buoyancy of the wake ($F_{sb}$) and the flow structure ($F_{sj}$). It is noted that $F_{sb}$ contributes primarily to the enhanced drag at the early stage, which becomes less dominant after the detachment of the wake. In contrast, $F_{sj}$ plays a pivotal role in reversing the particle's motion. We conjecture that the jet flow is a necessary condition for the occurrence of bouncing motion. Then, we examine the minimal velocities (negative values when bounce occurs) of the particle by varying the lower Reynolds number $Re_l$, the Froude number $Fr$ and the upper Reynolds number $Re_u$ within the ranges $1 \leq Re_l\leq 125$, $115 \leq Re_u\leq 356$ and $2 \leq Fr\leq 7$. We find that the bouncing behaviour is primarily determined by $Re_l$. In our experiments, the bouncing motion is found to occur below a critical lower Reynolds number around $Re^ \ast _{l}=30$. In the numerical simulations, the highest value for this critical number is $Re^ \ast _{l}=46.2$, limited in the currently studied parametric ranges.

physics.flu-dyn↗

arXiv2022

A topological realization of spin polarization through vortex formation in collisions of Bose-Einstein condensates

The global spin polarization of hadrons in heavy ion collisions has been measured in STAR (the Solenoidal Tracker At Relativistic heavy ion collider) experiments, which opens up a new window in the study of the hottest, least viscous and most vortical fluid that has ever been produced in the laboratory. We present a different approach to spin polarization from conventional ones: a topological realization of spin polarization through quantum vortex formation in collisions of Bose-Einstein condensates (BEC). This approach is based on the observation that the vortex is a topological excitation in a superfluid in presence of local orbital angular momentum and is an analogue of spin degrees of freedom. The formation processes of vortices and vortex-antivortex pairs are investigated by solving the Gross-Pitaevskii Equation with a large-scale parallel algorithm on Graphics Processing Unit (GPU) to very high precision. In a rotating environment, the primary vortex with winding number one is stable against perturbation, which has a minimal energy and fixed orbital angular momentum (OAM), but the vortices with larger winding numbers are unstable and will decay into primary vortices through a redistribution of the energy and vorticity. The injection of OAM can also be realized in non-central collisions of self-interacting condensates, part of the OAM of the initial state will induce the formation of vortices through concentration of energy and vorticity density around topological defects. Different from a hydrodynamical description, the interference of the wave function plays an important role in the transport of energy and vorticity, reflecting the quantum nature of the vortex formation process. The study of the vortex formation may shed light on the nature of particle spin and spin-orbit couplings in strong interaction matter produced in heavy-ion collisions.

nucl-th↗

arXiv2022

High precision solutions to quantized vortices within Gross-Pitaevskii equation

The dynamics of vortices in Bose-Einstein condensates of dilute cold atoms can be well formulated by Gross-Pitaevskii equation. To better understand the properties of vortices, a systematic method to solve the nonlinear differential equation for the vortex to a very high precision is proposed. Through two-point Pad$\acute{\text{e}}$ approximants, these solutions are presented in terms of simple rational functions, which can be used in the simulation of vortex dynamics. The precision of the solutions is sensitive to the connecting parameter and the truncation orders. It can be improved significantly with a reasonable extension in the order of rational functions. The errors of the solutions and the limitation of two-point Pad$\acute{\text{e}}$ approximants are discussed. This investigation may shed light on the exact solution to the nonlinear vortex equation.

nlin.PS↗

arXiv2021

Nonequilibrium kinetic freeze-out properties in relativistic heavy ion collisions from energies employed at the RHIC beam energy scan to those available at the LHC

In this paper, we investigate the kinetic freeze-out properties in relativistic heavy ion collisions at different collision energies. We present a study of standard Boltzmann-Gibbs Blast-Wave (BGBW) fits and Tsallis Blast-Wave (TBW) fits performed on the transverse momentum spectra of identified hadrons produced in Au + Au collisions at collision energies of $\sqrt{s_{\rm{NN}}}=$ 7.7 - 200 GeV at the Relativistic Heavy Ion Collider (RHIC), and in Pb + Pb collisions at collision energies of $\sqrt{s_{\rm{NN}}}=$ 2.76 and 5.02 TeV at the Large Hadron Collider (LHC). The behavior of strange and multi-strange particles is also investigated. We found that the TBW model describes data better than the BGBW one overall, and the contrast is more prominent as the collision energy increases as the degree of non-equilibrium of the produced system is found to increase. From TBW fits, the kinetic freeze-out temperature at the same centrality shows a weak dependence of collision energy between 7.7 and 39 GeV, while it decreases as collision energy continues to increase up to 5.02 TeV. The radial flow is found to be consistent with zero in peripheral collisions at RHIC energies but sizable at LHC energies and central collisions at all RHIC energies. We also observed that the strange hadrons, with higher temperature and similar radial flow, approach equilibrium more quickly from peripheral to central collisions than light hadrons. The dependence of temperature and flow velocity on non-equilibrium parameter ($q-1$) is characterized by two second-order polynomials. Both $a$ and $dξ$ from the polynomials fit, related to the influence of the system bulk viscosity, increase toward lower RHIC energies.

nucl-th↗

arXiv2020

Continuity of family of Calderón projections

We consider a continuous family of linear elliptic differential operators of arbitrary order over a smooth compact manifold with boundary. Assuming constant dimension of the spaces of inner solutions, we prove that the orthogonalized Calderón projections of the underlying family of elliptic operators form a continuous family of projections. Hence, its images (the Cauchy data spaces) form a continuous family of closed subspaces in the relevant Sobolev spaces. We use only elementary tools and classical results: basic manipulations of operator graphs and other closed subspaces in Banach spaces; elliptic regularity; Green's formula and trace theorems for Sobolev spaces; well-posed boundary conditions; duality of spaces and operators in Hilbert space; and the interpolation theorem for operators in Sobolev spaces. \keywords{Calder{ó}n projection\and Cauchy data spaces \and Elliptic differential operators \and Green's formula\and Interpolation theorem\and Manifolds with boundary\and Parameter dependence \and Trace theorem \and Variational properties

math.AP↗

arXiv2019

MLOD: A multi-view 3D object detection based on robust feature fusion method

This paper presents Multi-view Labelling Object Detector (MLOD). The detector takes an RGB image and a LIDAR point cloud as input and follows the two-stage object detection framework. A Region Proposal Network (RPN) generates 3D proposals in a Bird's Eye View (BEV) projection of the point cloud. The second stage projects the 3D proposal bounding boxes to the image and BEV feature maps and sends the corresponding map crops to a detection header for classification and bounding-box regression. Unlike other multi-view based methods, the cropped image features are not directly fed to the detection header, but masked by the depth information to filter out parts outside 3D bounding boxes. The fusion of image and BEV features is challenging, as they are derived from different perspectives. We introduce a novel detection header, which provides detection results not just from fusion layer, but also from each sensor channel. Hence the object detector can be trained on data labelled in different views to avoid the degeneration of feature extractors. MLOD achieves state-of-the-art performance on the KITTI 3D object detection benchmark. Most importantly, the evaluation shows that the new header architecture is effective in preventing image feature extractor degeneration.

cs.CV↗

arXiv2018

Off-equilibrium infrared structure of self-interacting scalar fields: Universal scaling, Vortex-antivortex superfluid dynamics and Bose-Einstein condensation

We map the infrared dynamics of a relativistic single component ($N=1$) interacting scalar field theory to that of nonrelativistic complex scalar fields. The Gross-Pitaevskii (GP) equation, describing the real time dynamics of single component ultracold Bose gases, is obtained at first nontrivial order in an expansion proportional to the powers of $λϕ^2/m^2$ where $λ$, $ϕ$ and $m$ are the coupling constant, the scalar field and the particle mass respectively. Our analytical studies are corroborated by numerical simulations of the spatial and momentum structure of overoccupied scalar fields in (2+1)-dimensions. Universal scaling of infrared modes, vortex-antivortex superfluid dynamics and the off-equilibrium formation of a Bose-Einstein condensate are observed. Our results for the universal scaling exponents are in agreement with those extracted in the numerical simulations of the GP equation. As in these simulations, we observe coarsening phase kinetics in the Bose superfluid with strongly anomalous scaling exponents relative to that of vertex resummed kinetic theory. Our relativistic field theory framework further allows one to study more closely the coupling between superfluid and normal fluid modes, specifically the turbulent momentum and spatial structure of the coupling between a quasi-particle cascade to the infrared and an energy cascade to the ultraviolet. We outline possible applications of the formalism to the dynamics of vortex-antivortex formation and to the off-equilibrium dynamics of the strongly interacting matter formed in heavy-ion collisions.

hep-th↗