Synthetic Data Generation

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43 papers in the last four weeks, up 59% on the four weeks before. 0.4% of all new papers.

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Latest papers 417

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SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data
May 28, 2026Joseph Stitt, Pratik Rathore, Madeleine Udell +1Synthetic Data Generation PDE Surrogate Modeling
Stanford University
Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning
May 28, 2026Tong Ye, Hang Yu, Tengfei Ma +6Domain Adaptation Synthetic Data Generation
vivo AI Lab, Ant Group, Zhejiang University
From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs
May 28, 2026Silin Zhou, Chenhao Wang, Yuntao Wen +3Trajectory Generation Intelligent Transportation Systems
University of Electronic Science and Technology of China Chengdu, China · King Abdullah University of Science and Technology Thuwal, Saudi Arabia
TraceCodec: A Compiler-Backed Neural Codec for Stateful Multi-Flow Network Traffic Traces
May 28, 2026Junhui Ding, Xinchen Zhang, Xiaohui Xie +1Synthetic Data Generation
Tsinghua University · University of Hong Kong
Make LLM Learn to Synthesize from Streaming Experiences through Feedback
May 28, 2026Zhenlin Hu, Yan Wang, Zhen Bi +7Continual Learning for LLMs Synthetic Data Generation
Huzhou Normal University · Alibaba Group · Zhejiang Key Laboratory of Intelligent Education Technology and Application +1
CityGen: Structure-Guided City-Style Synthesis for Cross-City Autonomous Driving
May 28, 2026Zezhong Qian, Zhao Yang, Lu Tan +4Domain Adaptation Conditional Image Generation
1Jiangsu Cytoderm Intelligent Technology Co., Ltd., China · 2Xi’an Jiaotong University, Xi’an, China · 3Tsinghua University, Beijing, China +1
BuilDyn: Excitation-Driven Data Generation for Building Thermal Dynamics Modeling and Control
May 28, 2026Felix Koch, Thomas Krug, Fabian Raisch +2Synthetic Data Generation System Identification
Technical University of Applied Sciences Rosenheim Rosenheim, Germany · Technical University of Munich Munich, Germany · Karlsruhe Institute of Technology Karlsruhe, Germany
MARTIAN: A Rendering Framework for Aerial Mars Imagery from HiRISE Orbital Data
May 28, 2026Dario Pisanti, Georgios GeorgakisVisual Navigation Aerial Robotics
Space Robotics Research Group, SnT, University of Luxembourg. · Jet Propulsion Laboratory, California Institute of Technology.
GPS-Enhanced Tourist Mobility Modeling with Seasonal Spatial Priors and LLM-Based Activity Chain Generation
May 28, 2026Yifan Liu, Yanling Sang, Xishun Liao +6Synthetic Data Generation
UCLA Mobility Lab, Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, USA. · University of Central Florida, Orlando, FL, USA. · Novateur Research Solutions, Ashburn, VA, USA.
Error as a Lens: Probing LLM Reasoning through Synthetic Misconception Generation
May 27, 2026Xinming Yang, Jun LiSynthetic Data Generation Generative AI in Education
CUNY Graduate Center · CUNY Queens College
Learn from Weaknesses: Automated Domain Specialization for Small Computer-Use Agents
May 27, 2026Suji Kim, Kangsan Kim, Sung Ju HwangComputer-Use Agents Synthetic Data Generation
KAIST · Samsung Electronics
Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection
May 27, 2026Vijeta Deshpande, Tootiya Giyahchi, Veena Padmanabhan +2Synthetic Data Generation Synthetic Data Generation for Language Models
UMass Lowell · Amazon · Amazon, CMU +1
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
May 27, 2026Junfeng Nie, Alvin Jin, Xiaohui ChenSynthetic Data Generation Synthetic Tabular Data Generation
AnyFluxion · University of Southern California
FedEHR-Gen: Federated Synthetic Time-Series EHR Generation via Latent Space Alignment and Distribution-Aware Aggregation
May 27, 2026Jun Bai, Ziyang Song, Yue LiSynthetic Data Generation Conditional Variational Autoencoder
McGill University Mila - Quebec AI Institute Montreal, Canada · Ohio University Athens, United States
Representation-Conditioned Diffusion Models for Guided Training Data Generation
May 26, 2026Nithesh Chandher Karthikeyan, Jonas Unger, Gabriel EilertsenSynthetic Data Augmentation Latent Diffusion Models
Linkoping University
High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework
May 26, 2026Giuseppe Masi, Andrea Coletta, Novella BartoliniSynthetic Data Generation Time Series Generation
Sapienza University of Rome, Italy · Banca d’Italia, Italy
Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning
May 26, 2026Yunbo Long, Ge Zheng, Liming Xu +1KG Construction Synthetic Data Generation
Department of Engineering, University of Cambridge, Cambridge, United Kingdom · The Alan Turing Institute, London, United Kingdom
FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis
May 26, 2026Peiyu Duan, Jiyao Wang, Nicha C. Dvornek +4Flow Matching Synthetic Data Generation
Department of Biomedical Engineering, Yale University, New Haven, CT, USA · Department of Radiology & Biomedical Imaging, Yale University, New Haven, CT, USA · Department of Electrical Engineering, Yale University, New Haven, CT, USA
CUA-Gym: Scaling Verifiable Training Environments and Tasks for Computer-Use Agents
May 25, 2026Bowen Wang, Dunjie Lu, Junli Wang +11Computer-Use Agents Synthetic Data Generation
The University of Hong Kong · Qwen Team, Alibaba Inc. · University of California, San Diego +1
TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis
May 24, 2026Festus KahunlaSynthetic Data Generation Synthetic Data Generation for Language Models
Drexel University · Pombo Labs
ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations
May 24, 2026Muhammad Naveed Riaz, Maciej Wielgosz, Antonio M. López PeñaAutonomous Driving Datasets Pedestrian Behavior Modeling
Departament de Ciències de la Computació and Computer Vision Center (CVC), Universitat Autònoma de Barcelona (UAB), Bellaterra, Barcelona, Spain · Institute of Electronics, Faculty of Computer Science, Electronics and Telecommunications, AGH University of Krakow, Kraków, Poland
PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs
May 22, 2026Rim Assouel, Amir Bar, Michal Drozdzal +1Visual Spatial Reasoning Synthetic Data Generation
1Mila - Québec AI Institute · 4McGill University · 3FAIR at Meta Superintelligence Labs +2
Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition
May 21, 2026Ganlin Feng, Yuxi Long, Erin Lou +4Rare Disease Diagnosis Synthetic Data Generation
1Western University · University of Toronto
Cell Phantom Video Generation in Elliptical Fourier Descriptor Domain
May 21, 2026Francesco Benedetto, Roberto Basla, Luca Magri +1Synthetic Data Generation Video Generation
Department of Electronics Information and Bioengineering, Politecnico di Milano, Italy
GazePrior: Zero-Shot AR/VR Eye Tracking via Learned 3D Gaze Reconstruction
May 21, 2026Corentin Dumery, David Colmenares, Alexander Fix +3Gaze Estimation Synthetic Data Generation
Meta Reality Labs, Redmond, USA · EPFL, Lausanne, Switzerland · Work done during an internship at Meta
MindLoom: Composing Thought Modes for Frontier-Level Reasoning Data Synthesis
May 20, 2026Haiyang Shen, Taian Guo, Xuanzhong Chen +11Synthetic Data Generation Structured Reasoning
1Peking University · 2Tsinghua University
OpenSeisML: Open Large-Scale Real Seismic and well-log Dataset for Generative AI
May 19, 2026Ipsita Bhar, Huseyin Tuna Erdinc, Thales Souza +2Synthetic Data Generation Training Data Curation
Georgia Institute of Technology, Atlanta, USA · Osokey Ltd, Henley-on-Thames, UK
A 10,000-Year Global Stochastic Tropical Cyclone Catalog with Wind-Dependent Track Transitions (WHITS)
May 19, 2026Jennifer Nakamura, Upmanu LallSynthetic Data Generation Sequence Modeling
Lamont-Doherty Earth Observatory of Columbia University, Ocean and Climate Physics, Palisades, New York, 10964 · Arizona State University, School of Complex Adaptive Systems, Tempe, Arizona, 85287 · Columbia University, Earth and Environmental Engineering, New York, New York, 10027
Physics-informed simulation framework for realistic sonar image generation and statistical validation
May 19, 2026Kamal Basha S, Athira NambiarUnderwater Imaging Synthetic Data Generation
Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, India
Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Construction
May 19, 2026Zongwu Xie, Yonglong Zhang, Yifan Yang +2Synthetic-to-Real Domain Adaptation Synthetic Data Generation
State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

May 28, 2026, cs.LGJ/K move · Enter open · S save

SubsurfaceGen: Procedural Generation of Field-Scale Earth Models and Seismic Data

Joseph Stitt, Pratik Rathore, Madeleine Udell, Ching-Yao Lai

Stanford University

Synthetic Data Generation PDE Surrogate Modeling Synthetic Benchmark Generation Seismology Inverse Problems Image Inverse Problems

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Full waveform inversion (FWI) is the gold standard for subsurface imaging, with applications from carbon sequestration to energy and mineral exploration to earthquake hazard assessment. Machine learning approaches to FWI need field-scale, geologically diverse, and physically realistic training data, but existing resources such as Marmousi, SEAM, and OpenFWI fall short on spatial extent, temporal extent, geological diversity, and physical realism. We address these limitations with SubsurfaceGen, a GPU-accelerated generator for 3D velocity models and seismic data. Along with SubsurfaceGen, we release a paired dataset of 4,276 2D velocity slices, 5 s wavefields, and 8 s shot gathers drawn from 42 realistic, field-scale 3D velocity models, each spanning 10 km x 10 km laterally and 6.19 km deep at 10 m resolution. The dataset spans six geological settings -- four built with SubsurfaceGen and two drawn from prior sources -- relevant for carbon sequestration and hydrocarbon exploration. We use this dataset to evaluate neural operators on wavefield prediction and encoder-decoders on end-to-end velocity inversion, holding out one geological setting for out-of-distribution testing. These experiments surface failure modes at field-scale and demonstrate how SubsurfaceGen and the associated dataset can impact ML-based FWI.

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Latest papers 417