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Researcher profile

Chi Chen

Chi Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputation and Languagecond-mat.mtrl-sciEmerging TechnologiesMachine Learningphysics.comp-phquant-ph

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Published work

3 published item(s)

preprint2026arXiv

A joint voxel flow - phase field framework for ultra-long microstructure evolution prediction with physical regularization

Phase-field (PF) modeling is a powerful tool for simulating microstructure evolution. To overcome the high computational cost of PF in solving complex PDEs, machine learning methods such as PINNs, convLSTM have been used to predict PF evolution. However, current methods still face shortages of low flexibility, poor generalization and short predicting time length. In this work, we present a joint framework coupling voxel-flow network (VFN) with PF simulations in an alternating manner for long-horizon temporal prediction of microstructure evolution. The VFN iteratively predicts future evolution by learning the flow of pixels from past snapshots, with periodic boundaries preserved in the process. Periodical PF simulations suppresses nonphysical artifacts, reduces accumulated error, and extends reliable prediction time length. The VFN is about 1,000 times faster than PF simulation on GPU. In validation using grain growth and spinodal decomposition, MSE and SSIM remain 6.76% and 0.911 when predicted 18 frames from only 2 input frames, outperforming similar predicting methods. For an ultra-long grain growth prediction for 82 frames from 2 input frames, grain number decreases from 600 to 29 with NMSE of average grain area remaining 1.64%. This joint framework enables rapid, generalized, flexible and physically consistent microstructure forecasting from image-based data for ultra-long time scales.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion

With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. A primary reason for this shortcoming is that the visual features for each images are encoded individually by frozen encoders before feeding into the LLM backbone, lacking awareness of other images and the multimodal instructions. We term this issue as prior-LLM modality isolation and propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion prior to feeding the features into LLMs. This paradigm initially "browses" through the inputs for essential insights, and then revisits the inputs to "concentrate" on crucial details, guided by these insights, to achieve a more comprehensive understanding of the multimodal inputs. Additionally, we develop training strategies specifically to enhance the understanding of multi-image inputs. Our method markedly boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models

We present an experimental study of energy-to-solution (ETS) of hybrid quantum-classical applications, enabled by direct instrumentation of power consumption of a Forte Enterprise trapped-ion quantum processor. We apply this methodology to a hybrid quantum-classical pipeline for quantum fine-tuning of foundational AI models, and validate the approach end-to-end on quantum hardware. Despite noise and limited qubit counts, the resulting models achieve accuracy competitive with and exceeding classical baselines such as logistic regression and support vector classifiers. Our results show that QPU energy consumption scales approximately linearly with qubit number for shallow circuits, while classical simulation exhibits exponential scaling, indicating a break-even for ETS around 34 qubits. The classification error improvement of the best quantum fine-tuned model over the best classical fine-tuned model considered in this study is around 24%. We further contextualize these findings with comparisons to tensor network methods. This work establishes energy-to-solution as a measurable and scalable metric for evaluating quantum applications and provides experimental evidence of favorable energy-accuracy trade-offs.

0 citations0 reviewsNo code linkedOpen access