arXiv subjects

Jiao Liu

Publications and source records attributed to Jiao Liu.

28 records · Page 2Linked to original sources

arXiv2025

LLM2TEA: An Agentic AI Designer for Discovery with Generative Evolutionary Multitasking

This paper presents LLM2TEA, a Large Language Model (LLM) driven MultiTask Evolutionary Algorithm, representing the first agentic AI designer of its kind operating with generative evolutionary multitasking (GEM). LLM2TEA enables the crossbreeding of solutions from multiple domains, fostering novel solutions that transcend disciplinary boundaries. Of particular interest is the ability to discover designs that are both novel and conforming to real-world physical specifications. LLM2TEA comprises an LLM to generate genotype samples from text prompts describing target objects, a text-to-3D generative model to produce corresponding phenotypes, a classifier to interpret its semantic representations, and a computational simulator to assess its physical properties. Novel LLM-based multitask evolutionary operators are introduced to guide the search towards high-performing, practically viable designs. Experimental results in conceptual design optimization validate the effectiveness of LLM2TEA, showing 97% to 174% improvements in the diversity of novel designs over the current text-to-3D baseline. Moreover, over 73% of the generated designs outperform the top 1% of designs produced by the text-to-3D baseline in terms of physical performance. The designs produced by LLM2TEA are not only aesthetically creative but also functional in real-world contexts. Several of these designs have been successfully 3D printed, demonstrating the ability of our approach to transform AI-generated outputs into tangible, physical designs. These designs underscore the potential of LLM2TEA as a powerful tool for complex design optimization and discovery, capable of producing novel and physically viable designs.

cs.AI↗

arXiv2025

Multi-Objective Recommendation in the Era of Generative AI: A Survey of Recent Progress and Future Prospects

With the recent progress in generative artificial intelligence (Generative AI), particularly in the development of large language models, recommendation systems are evolving to become more versatile. Unlike traditional techniques, generative AI not only learns patterns and representations from complex data but also enables content generation, data synthesis, and personalized experiences. This generative capability plays a crucial role in the field of recommendation systems, helping to address the issue of data sparsity and improving the overall performance of recommendation systems. Numerous studies on generative AI have already emerged in the field of recommendation systems. Meanwhile, the current requirements for recommendation systems have surpassed the single utility of accuracy, leading to a proliferation of multi-objective research that considers various goals in recommendation systems. However, to the best of our knowledge, there remains a lack of comprehensive studies on multi-objective recommendation systems based on generative AI technologies, leaving a significant gap in the literature. Therefore, we investigate the existing research on multi-objective recommendation systems involving generative AI to bridge this gap. We compile current research on multi-objective recommendation systems based on generative techniques, categorizing them by objectives. Additionally, we summarize relevant evaluation metrics and commonly used datasets, concluding with an analysis of the challenges and future directions in this domain.

cs.IR↗

arXiv2025

Strong and weak convergence orders of numerical methods for SDEs driven by time-changed Lévy noise

This work investigates the strong and weak convergence orders of numerical methods for SDEs driven by time-changed Lévy noise under the globally Lipschitz conditions. Based on the duality theorem, we prove that the numerical approximation generated by the stochastic $θ$ method with $θ\in [0,1]$ and the simulation of inverse subordinator converges strongly with order $1/2$. Moreover, the numerical approximation combined with the Euler--Maruyama method and the estimate of inverse subordinator is shown to have the weak convergence order $1$ by means of the Kolmogorov backward partial integro differential equations. These theoretical results are finally confirmed by some numerical experiments.

math.NA↗

arXiv2025

Dynamic modulation of dual-band nonreciprocal radiation in a graphene-Weyl semimetal plasmonic structure

We introduce and develop a hybrid structure combining graphene and Weyl semimetal, capable of achieving dynamically adjustable dual-band nonreciprocal radiation. The results reveal that the nonreciprocal radiation can be attributed to the synergistic interaction between resonance mode excitation and the unique properties of Weyl materials, with the electric field distribution providing further insights into the graphene plasmon modes involved. By exploiting the resonant characteristics of graphene plasmons, we demonstrate that strong nonreciprocal radiation can be effectively regulated through adjusting the grating's geometric parameters, while maintaining robustness over a wide range. Notably, substantial dynamic tuning of the resonant wavelength for nonreciprocal radiation is achievable by modulating the Fermi level of graphene. Our research results offer promising prospects for the developing of complex energy harvesting and conversion systems within advanced thermal frameworks.

physics.optics↗

arXiv2024

Bayesian Inverse Transfer in Evolutionary Multiobjective Optimization

Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of trade-off solutions is sought under tight evaluation budgets. In this paper, we introduce a novel concept of \textit{inverse transfer} in multiobjective optimization. Inverse transfer stands out by employing Bayesian inverse Gaussian process models to map performance vectors in the objective space to population search distributions in task-specific decision space, facilitating knowledge transfer through objective space unification. Building upon this idea, we introduce the first Inverse Transfer Evolutionary Multiobjective Optimizer (invTrEMO). A key highlight of invTrEMO is its ability to harness the common objective functions prevalent in many application areas, even when decision spaces do not precisely align between tasks. This allows invTrEMO to uniquely and effectively utilize information from heterogeneous source tasks as well. Furthermore, invTrEMO yields high-precision inverse models as a significant byproduct, enabling the generation of tailored solutions on-demand based on user preferences. Empirical studies on multi- and many-objective benchmark problems, as well as a practical case study, showcase the faster convergence rate and modelling accuracy of the invTrEMO relative to state-of-the-art evolutionary and Bayesian optimization algorithms. The source code of the invTrEMO is made available at https://github.com/LiuJ-2023/invTrEMO.

cs.NE↗

arXiv2024

Precise-Physics Driven Text-to-3D Generation

Text-to-3D generation has shown great promise in generating novel 3D content based on given text prompts. However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes. This greatly hinders the practicality of generated 3D shapes in real-world applications. In this work, we propose Phy3DGen, a precise-physics-driven text-to-3D generation method. By analyzing the solid mechanics of generated 3D shapes, we reveal that the 3D shapes generated by existing text-to-3D generation methods are impractical for real-world applications as the generated 3D shapes do not conform to the laws of physics. To this end, we leverage 3D diffusion models to provide 3D shape priors and design a data-driven differentiable physics layer to optimize 3D shape priors with solid mechanics. This allows us to optimize geometry efficiently and learn precise physics information about 3D shapes at the same time. Experimental results demonstrate that our method can consider both geometric plausibility and precise physics perception, further bridging 3D virtual modeling and precise physical worlds.

cs.CV↗

arXiv2023

CS-PCN: Context-Space Progressive Collaborative Network for Image Denoising

Currently, image-denoising methods based on deep learning cannot adequately reconcile contextual semantic information and spatial details. To take these information optimizations into consideration, in this paper, we propose a Context-Space Progressive Collaborative Network (CS-PCN) for image denoising. CS-PCN is a multi-stage hierarchical architecture composed of a context mining siamese sub-network (CM2S) and a space synthesis sub-network (3S). CM2S aims at extracting rich multi-scale contextual information by sequentially connecting multi-layer feature processors (MLFP) for semantic information pre-processing, attention encoder-decoders (AED) for multi-scale information, and multi-conv attention controllers (MCAC) for supervised feature fusion. 3S parallels MLFP and a single-scale cascading block to learn image details, which not only maintains the contextual information but also emphasizes the complementary spatial ones. Experimental results show that CS-PCN achieves significant performance improvement in synthetic and real-world noise removal.

cs.CV↗

arXiv2022

Unraveling the Scotogenic Model at Muon Collider

The Scotogenic model extends the standard model with three singlet fermion $N_i$ and one inert doublet scalar $η$ to address the common origin of tiny neutrino mass and dark matter. For fermion dark matter $N_1$, a hierarchical Yukawa structure $|y_{1e}|\ll|y_{1μ}|\sim|y_{1τ}|\sim\mathcal{O}(1)$ is usually favored to satisfy constraints from lepton flavor violation and relic density. Such large $μ$-related Yukawa coupling would greatly enhance the pair production of charged scalar $η^\pm$ at the muon collider. In this paper, we investigate the dilepton signature of the Scotogenic model at a 14 TeV muon collider. For the dimuon signature $μ^+μ^-+/ \hspace{-0.65em} E_T$, we find that most viable samples can be probed with $200~\text{fb}^{-1}$ data. The ditau signature $τ^+τ^-+/ \hspace{-0.65em}E_T$ is usually less promising but is important to probe the small $|y_{1μ}|$ region. The mono-photon signature $γ+/ \hspace{-0.65em} E_T$ could also probe the compressed mass region $M_1\lesssim M_{η^\pm}$. Masses of charged scalar $η^\pm$ and dark matter $N_1$ can be further extracted by a binned likelihood fit of the dilepton energy.

hep-ph↗

arXiv2022

Free-space point-to-multiplepoint optical frequency transfer with lens assisted integrated beam steering

We report on the realization of high-performance silica integrated two-dimensional lens assisted beam-steering (LABS) arrays along with the first-of-their-kind point-to-multiplepoint optical frequency transfer. {The LABS equips with $N$ antennas} and has the capability to produce arbitrary number of output beams with different output angles with the simple control complexity. We demonstrate that the LABS has 16 scanning angles, which can support {the access capability for the maximum of simultaneous 16 user nodes.} The coaxial configuration for transmitting and receiving the light as a monolithic transceiver allows us to reduce the out-of-loop phase noise significantly. Finally, the LABS-based non-blocking point-to-multiplepoint in-door free-space optical frequency transfer links with 24 m and 50 m free-space links are shown. After being compensated for the free-space link up to 50 m, the fractional frequency instability of $4.5\times10^{-17}$ and $7.7\times10^{-20}$ at the averaging time of 1 s and 20,000 s, respectively, can be achieved. The present work proves the potential application of the 2D LABS in free-space optical time-frequency transfer and provides a guidance for developing a chip-scale optical time-frequency transfer system.

physics.ins-det↗

arXiv2021

A diffuse-interface lattice Boltzmann method for fluid-particle interaction problems

In this paper, a diffuse-interface lattice Boltzmann method (DI-LBM) is developed for fluid-particle interaction problems. In this method, the sharp interface between the fluid and solid is replaced by a thin but nonzero thickness transition region named diffuse interface, where the physical variables varies continuously. In order to describe the diffuse interface, we introduce a smooth function, which is similar to the order parameter in phase-field model or the volume fraction of solid phase in the partially saturated lattice Boltzmann method (PS-LBM). In addition, to depict the fluid-particle interaction more accurately, a modified force term is also proposed and included in the evolution equation of the DI-LBM. Some classical problems are used to test the DI-LBM, and the results are in good agreement with some available theoretical and numerical works. Finally, it is also found that the DI-LBM is more efficient and accurate than the PS-LBM with the superposition model.

physics.comp-ph↗