RealRain · AI4Science · 计算机视觉/多模态 RealRain · AI4Science · Computer Vision & Multimodal

徐北辰 Beichen Xu

非雨(ThisRainIsNotARealRain)创始人/CEO。我们在RealRain上以“工业4.0”的理念构建动漫内容生产的数字孪生系统与多Agent 协作产线,让高质量内容生产成为稳定、可规模化的协作流程。我的技术背景来自 AI4Science、计算机视觉与多模态、Physics ML 与可复现工程化训练流程。 Founder/CEO of ThisRainIsNotARealRain. With RealRain, we’re building an Industry 4.0–inspired digital-twin and multi-agent production system for animation—making high-quality creation a stable, scalable workflow. My background spans AI4Science, computer vision & multimodal learning, physics ML, and reproducible engineering pipelines.

位置Location

北京海淀Haidian, Beijing

方向Focus

AIGC · AI4Science

工作Role

创始人/CEOFounder/CEO

概览 Snapshot

方向Focus
RealRain AIGC Systems · Computer Vision & Multimodal · AI4Science
状态Availability
开放合作 Open to collaborate

关于About

我现在在创业,担任非雨(ThisRainIsNotARealRain)创始人/CEO。我们面向动漫内容生产的工业化升级,在RealRain上构建“可控、可编辑、可协作、可规模化”的 AIGC 多Agent 生产系统:将传统产线映射为可模拟/回放/调度的数字孪生产线,并用多模态资产管理与一致性控制沉淀长期内容资产。我的工程与研究经验来自计算机视觉/视觉语言与多模态、AI4Science 与 Physics ML,擅长把研究原型推进为可复现、可交付的系统。 I’m currently building ThisRainIsNotARealRain as Founder/CEO. With RealRain, we’re upgrading animation production via a controllable, editable, collaborative, and scalable AIGC multi-agent system: a digital-twin pipeline with simulation/replay/scheduling, plus multimodal asset management and consistency control for long-term content assets. My background spans computer vision/vision-language and multimodal learning, AI4Science, and physics ML—bridging research prototypes into reproducible, shippable systems.

项目Projects

AeroDiT(用于 RANS 的扩散 Transformer) AeroDiT (Diffusion Transformers for RANS)

将扩散模型与Transformer结合,用于 Reynolds-Averaged Navier–Stokes 空气动力学仿真(翼型流动)。 Diffusion Transformers for Reynolds-Averaged Navier–Stokes simulations of airfoil flows.

  • 扩散模型Diffusion
  • TransformerTransformers
  • 流体力学Fluid Dynamics

MRes 项目 · 引力波 WGAN MRes Project · Gravitational Wave WGAN

模拟多源引力波时序信号,训练 WGAN 以从信号中重建参数;使用 HPC 进行训练与可视化分析。 Simulated multi-source gravitational-wave time series and trained WGANs to reconstruct parameters; trained and analyzed on HPC.

  • PyTorch
  • PyTorch Lightning
  • 高性能计算HPC

多模态 GIS / 遥感训练流程 Multimodal GIS / Remote Sensing Pipelines

面向大规模地理遥感任务(道路提取、轨迹建模、卫星影像理解)构建 VL/多模态训练与评估流程,包含 LoRA 与全参微调实验。 Built vision–language / multimodal pipelines for large-scale GIS and remote sensing tasks (road extraction, trajectory modeling, satellite understanding), including LoRA and full fine-tuning experiments.

  • 视觉语言Vision–Language
  • LoRA
  • 多 GPUMulti-GPU

机器学习增强的贝叶斯推断(嵌套采样) ML-enhanced Bayesian Inference (Nested Sampling)

在宇宙学推断中结合嵌套采样与机器学习(masked aggressive flow),用于后验分布生成与参数估计;并参与维护开源 Python 包。 ML-enhanced Bayesian inference for cosmology: nested sampling + masked aggressive flow for posterior generation and parameter inference; contributed to an open-source Python package.

  • 贝叶斯Bayesian
  • 嵌套采样Nested Sampling
  • Python

经历Experience

  1. 创始人 / CEO · 非雨(AIGC 创业) Founder / CEO · ThisRainIsNotARealRain (AIGC startup)

    至今Present

    • 以“工业4.0”思路构建动漫内容生产的数字孪生系统:把传统产线映射为可模拟/回放/调度的人机协作流程,实现稳定交付与可规模化协作。 Building an Industry 4.0–inspired digital-twin system for animation production: a controllable, schedulable human–agent pipeline for consistent, scalable delivery.
    • 多Agent 协作层映射不同基础模型能力,按产线节点分工协作;强调可替换、可升级、边界可控(系统而非玩具)。 Designing a multi-agent orchestration layer that maps to foundation-model capabilities across pipeline stages; modular, swappable, and controllable (system, not a toy).
    • 搭建多模态资产管理与检索体系:资产对齐、可演化、世界观/设定一致性管理,为长期内容资产与复用打底。 Building multimodal asset management and retrieval with alignment and consistency control for long-term reusable content assets.
  2. AI 算法工程师 · MapTech(北京) AI Algorithm Engineer · MapTech (Beijing)

    • 计算机视觉/视觉语言方向:构建 VL 与多模态学习流程,面向 GIS 与遥感任务(道路提取、轨迹建模、卫星影像理解)。Computer vision & vision–language: built VL/multimodal pipelines for GIS and remote sensing (road extraction, trajectory modeling, satellite understanding).
    • 主导全参微调与 LoRA 轻量适配实验(如 Qwen-VL),提升跨区域/跨模态数据集效率。Led full fine-tuning and LoRA adaptation experiments (e.g., Qwen-VL) to improve efficiency across heterogeneous geospatial datasets.
    • 设计地理特征 tokenization 与融合策略(patch、轨迹 embedding、hint maps),支撑分割与推理任务。Designed geospatial feature tokenization and fusion (image patches, trajectory embeddings, hint maps) for segmentation and reasoning.
    • 搭建可复现的多GPU训练/评估工作流,优化数据标注对齐与 benchmarking。Built reproducible multi-GPU training/evaluation workflows and improved curation, alignment, and benchmarking.
    • 与产品/工程团队合作,将研究原型落地为生产 pipeline 组件。Collaborated with product/engineering to translate prototypes into deployable components.
  3. AI 算法研究员 · Science42.tech(北京) AI Algorithm Researcher · Science42.tech (Beijing)

    • 研究 Transformers / Diffusion / GNN 在流体、医学与能源等科学场景的应用。Researched Transformers/Diffusion/GNNs for scientific domains including fluids, biomedicine, and energy systems.
    • 与清华大学、西湖大学、IMEC(比利时)等团队保持学术合作与交流。Maintained close collaborations with Tsinghua, Westlake University, and IMEC (Belgium) through joint studies and exchanges.
    • 开发 physics-informed AI 模型并推动高质量论文产出(投稿至《Physics of Fluids》等)。Developed physics-informed AI models and contributed to high-quality publications (e.g., submissions to Physics of Fluids).
    • 与跨学科团队协作研究代码与流程,并指导实习生完成科研与技术任务。Worked with interdisciplinary teams on research pipelines and mentored interns on technical/research tasks.
  4. 暑期实习 · Kavli Institute of Cosmology(剑桥) Summer Intern · Kavli Institute of Cosmology (Cambridge)

    • 研究 ML 增强的 Bayesian inference:使用 nested sampling 训练宇宙学数据,并用 masked aggressive flow 生成后验。Worked on ML-enhanced Bayesian inference for cosmology: nested sampling + masked aggressive flow for posterior generation.
    • 参与开源 Python 包 “margarine” 的贡献与协作。Contributed to the “margarine” Python package on GitHub.
  5. 暑期实习 · 紫金山天文台(南京) Summer Intern · Purple Mountain Observatory (Nanjing)

    • 对 GRB 现象做系统性综述;用 Python 对 Swift GRB 数据进行拟合与分析。Reviewed GRB literature and analyzed Swift GRB data with Python (fitting and statistical analysis).
    • 使用统计方法改进参数估计,熟悉 iminuit 的最小化与误差分析流程。Applied statistical methods for parameter estimation; used iminuit for minimization and uncertainty analysis.

摘要Highlights

  • 方向:RealRain AIGC 系统 / 计算机视觉与多模态 / AI4Science Focus: RealRain AIGC systems / computer vision & multimodal / AI4Science
  • 能力:模型研发、数据融合、可复现训练与评估、多GPU工作流 Strengths: modeling, data fusion, reproducible training & evaluation, multi-GPU workflows
  • 合作:跨学科协作,把原型推进到可交付组件 Collaboration: cross-functional work from prototype to deployable components
  • TransformerTransformers
  • 扩散模型Diffusion
  • 图神经网络GNN
  • LoRA
  • PyTorch

技能Skills

编程 / 框架Programming

  • Python, C++, Mathematica, SQL
  • PyTorch, TensorFlow

工程工具Tooling

  • Linux / Unix / Bash / vim
  • git / LaTeX / Docker

语言与奖项Languages & Awards

语言Languages

  • 中文:母语Chinese: Native
  • 英文:流利(口语与写作)English: Fluent (spoken and written)
  • 日语:N3(中级)Japanese: Intermediate (JLPT N3)

奖项Awards

  • 2018 · 未来科学家大奖(银奖)2018 · Award Program for Future Scientists — Silver medal
  • 2019 · 江苏省物理奥赛一等奖2019 · Physics Olympiad (Jiangsu, China) — First prize
  • 2020 · 南通市长科技创新奖2020 · Nantong Mayor’s Award for Science & Technology Innovation