The Design Computation and Digital Engineering (DeCoDE) Lab at MIT Mechanical Engineering advances the science of AI-driven design. We envision a future where humans and AI design together to tackle the world's most pressing challenges. To realize this vision, we develop fundamental machine learning and optimization methods that enhance the design of complex systems and support human teams in creating better products.
Our goal is to build versatile approaches that apply across scales, levels of complexity, wickedness, and disciplines. We frame design problems as generalizable machine learning and optimization tasks, enabling new ways to explore, evaluate, and generate solutions. By harnessing advanced AI, we are reimagining the product design process—accelerating innovation and shaping the next generation of engineering design.
In ICLR 2026
Bayesian optimization (BO) effectively optimizes expensive black-box functions but faces significant challenges in high-dimensional spaces (dimensions exceeding 100) due to the curse of dimensionality. Existing high-dimensional BO methods typically leverage low-dimensional embeddings or structural assumptions to mitigate this challenge, yet these approaches frequently incur considerable computational overhead and rigidity due to iterative surrogate retraining and fixed assumptions. To address these limitations, we propose Gradient-Informed Bayesian Optimization using Tabular Foundation Models (GIT-BO), an approach that utilizes a pre-trained tabular foundation model (TFM) as a surrogate, leveraging its gradient information to adaptively identify low-dimensional subspaces for optimization. We propose a way to exploit internal gradient computations from the TFM's forward pass by creating a gradient-informed diagnostic matrix that reveals the most sensitive directions of the TFM's predictions, enabling optimization in a continuously re-estimated active subspace without the need for repeated model retraining. Extensive empirical evaluation across 23 synthetic and real-world benchmarks demonstrates that GIT-BO consistently outperforms four state-of-the-art Gaussian process-based high-dimensional BO methods, showing superior scalability and optimization performances, especially as dimensionality increases up to 500 dimensions. This work establishes foundation models, augmented with gradient-informed adaptive subspace identification, as highly competitive alternatives to traditional Gaussian process-based approaches for high-dimensional Bayesian optimization tasks.
In Computer Methods in Applied Mechanics and Engineering 2026
A major obstacle to establishing reliable structure-property (SP) linkages in materials engineering is the scarcity of diverse 3D microstructure datasets. Limited dataset availability and insufficient control over the analysis and design space restrict the variety of achievable microstructure morphologies, hindering progress in solving the inverse (property-to-structure) design problem. To address these challenges, we introduce MicroLad, a latent diffusion framework specifically designed for reconstructing 3D microstructures from 2D data. Trained on 2D images and employing multi-plane denoising diffusion sampling in the latent space, the framework reliably generates stable and coherent 3D volumes that remain statistically consistent with the original data. While this reconstruction capability enables dimensionality expansion (2D-to-3D) for generating statistically equivalent 3D samples from 2D data, effective exploration of microstructure design requires methods to guide the generation process toward specific objectives. To achieve this, MicroLad integrates score distillation sampling (SDS), which combines a differentiable score loss with microstructural descriptor-matching and property-alignment terms. This approach updates encoded 2D slices of the 3D volume in the latent space, enabling robust inverse-controlled 2D-to-3D microstructure generation. Consequently, the method facilitates exploration of an expanded 3D microstructure analysis and design space in terms of both microstructural descriptors and material properties.
In NeurIPS 2025
Structural topology optimization (TO) is central to engineering design but remains computationally intensive due to complex physics and hard constraints. Existing deep-learning methods are limited to fixed square grids, a few hand-coded boundary conditions, and post-hoc optimization, preventing general deployment. We introduce Optimize Any Topology (OAT), a foundation-model framework that directly predicts minimum-compliance layouts for arbitrary aspect ratios, resolutions, volume fractions, loads, and fixtures. OAT combines a resolution- and shape-agnostic autoencoder with an implicit neural-field decoder and a conditional latent-diffusion model trained on OpenTO, a new corpus of 2.2 million optimized structures covering 2 million unique boundary-condition configurations. On four public benchmarks and two challenging unseen tests, OAT lowers mean compliance up to 90% relative to the best prior models and delivers sub-1 second inference on a single GPU across resolutions from 64 x 64 to 256 x 256 and aspect ratios as high as 10:1. These results establish OAT as a general, fast, and resolution-free framework for physics-aware topology optimization and provide a large-scale dataset to spur further research in generative modeling for inverse design.
In NeurIPS 2025
Computer-Aided Design (CAD) is a time-consuming and complex process, requiring precise, long-horizon user interactions with intricate 3D interfaces. While recent advances in AI-driven user interface (UI) agents show promise, most existing datasets and methods focus on short, low-complexity tasks in mobile or web applications, failing to capture the demands of professional engineering tools. In this work, we introduce VideoCAD, the first attempt at engineering UI interaction learning for precision tasks. Specifically, VideoCAD is a large-scale synthetic dataset consisting of over 41K annotated video recordings of CAD operations, generated using an automated framework for collecting high-fidelity UI action data from human-made CAD designs. Compared to existing datasets, VideoCAD offers an order of magnitude higher complexity in UI interaction learning for real-world engineering tasks, having up to a 20x longer time horizon than other datasets. We show two important downstream applications of VideoCAD: learning UI interactions from professional precision 3D CAD tools and a visual question-answering (VQA) benchmark designed to evaluate multimodal large language models' (LLM) spatial reasoning and video understanding abilities. To learn the UI interactions, we propose VideoCADFormer - a state-of-the-art model in learning CAD interactions directly from video, which outperforms multiple behavior cloning baselines. Both VideoCADFormer and the VQA benchmark derived from VideoCAD reveal key challenges in the current state of video-based UI understanding, including the need for precise action grounding, multi-modal and spatial reasoning, and long-horizon dependencies.
In IDETC 2025
BlendedNet is a publicly available aerodynamic dataset of 999 blended wing body (BWB) geometries. Each geometry is simulated across about nine flight conditions, yielding 8830 converged RANS cases with the Spalart-Allmaras model and 9 to 14 million cells per case. The dataset is generated by sampling geometric design parameters and flight conditions, and includes detailed pointwise surface quantities needed to study lift and drag. We also introduce an end-to-end surrogate framework for pointwise aerodynamic prediction. The pipeline first uses a permutation-invariant PointNet regressor to predict geometric parameters from sampled surface point clouds, then conditions a Feature-wise Linear Modulation (FiLM) network on the predicted parameters and flight conditions to predict pointwise coefficients Cp, Cfx, and Cfz. Experiments show low errors in surface predictions across diverse BWBs. BlendedNet addresses data scarcity for unconventional configurations and enables research on data-driven surrogate modeling for aerodynamic design.
In IDETC 2025
Efficient creation of accurate and editable 3D CAD models is critical in engineering design, significantly impacting cost and time-to-market in product innovation. Current manual workflows remain highly time-consuming and demand extensive user expertise. While recent developments in AI-driven CAD generation show promise, existing models are limited by incomplete representations of CAD operations, inability to generalize to real-world images, and low output accuracy. This paper introduces CAD-Coder, an open-source Vision-Language Model (VLM) explicitly fine-tuned to generate editable CAD code (CadQuery Python) directly from visual input. Leveraging a novel dataset that we created--GenCAD-Code, consisting of over 163k CAD-model image and code pairs--CAD-Coder outperforms state-of-the-art VLM baselines such as GPT-4.5 and Qwen2.5-VL-72B, achieving a 100% valid syntax rate and the highest accuracy in 3D solid similarity. Notably, our VLM demonstrates some signs of generalizability, successfully generating CAD code from real-world images and executing CAD operations unseen during fine-tuning. The performance and adaptability of CAD-Coder highlights the potential of VLMs fine-tuned on code to streamline CAD workflows for engineers and designers.
In IDETC 2025
The subjective evaluation of early stage engineering designs, such as conceptual sketches, traditionally relies on human experts. However, expert evaluations are time-consuming, expensive, and sometimes inconsistent. Recent advances in vision-language models (VLMs) offer the potential to automate design assessments, but it is crucial to ensure that these AI ``judges'' perform on par with human experts. However, no existing framework assesses expert equivalence. This paper introduces a rigorous statistical framework to determine whether an AI judge's ratings match those of human experts. We apply this framework in a case study evaluating four VLM-based judges on key design metrics (uniqueness, creativity, usefulness, and drawing quality). These AI judges employ various in-context learning (ICL) techniques, including uni- vs. multimodal prompts and inference-time reasoning. The same statistical framework is used to assess three trained novices for expert-equivalence. Results show that the top-performing AI judge, using text- and image-based ICL with reasoning, achieves expert-level agreement for uniqueness and drawing quality and outperforms or matches trained novices across all metrics. In 6/6 runs for both uniqueness and creativity, and 5/6 runs for both drawing quality and usefulness, its agreement with experts meets or exceeds that of the majority of trained novices. These findings suggest that reasoning-supported VLM models can achieve human-expert equivalence in design evaluation. This has implications for scaling design evaluation in education and practice, and provides a general statistical framework for validating AI judges in other domains requiring subjective content evaluation.
In Applied Energy 2025
Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are required to optimize power generation and reduce the Levelized Cost of Energy (LCoE), which represents the average cost of electricity over a project’s lifetime. However, upscaling turbines introduces engineering challenges, particularly in the design of supporting structures, especially towers. These towers must support increased loads while maintaining structural integrity, cost-efficiency, and transportability, making them essential to offshore wind projects’ success. This paper presents a comprehensive review of the latest advancements, challenges, and future directions driven by Artificial Intelligence (AI) in the design optimization of Offshore Wind Turbine (OWT) structures, with a focus on towers. It provides an in-depth background on key areas such as design types, load types, analysis methods, design processes, monitoring systems, Digital Twin (DT), software, standards, reference turbines, economic factors, and optimization techniques. Additionally, it includes a state-of-the-art review of optimization studies related to tower design optimization, presenting a detailed examination of turbine, software, loads, optimization method, design variables and constraints, analysis, and findings, motivating future research to refine design approaches for effective turbine upscaling and improved efficiency. Lastly, the paper explores future directions where AI can revolutionize tower design optimization, enabling the development of efficient, scalable, and sustainable structures. By addressing the upscaling challenges and supporting the growth of renewable energy, this work contributes to shaping the future of offshore wind turbine towers and others supporting structures.
In IDETC 2025
We introduce the concept of 'Design Agents' for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines.
In NeurIPS 2024
We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations. The dataset includes diverse car configurations such as fastback, notchback, and es- tateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles. Each entry in the dataset features detailed 3D meshes, parametric models, aerodynamic coefficients, and extensive flow and surface field data, along with segmented parts for car classification and point cloud data. This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification. With more than 39 TB of publicly available engineering data, DrivAerNet++ fills a significant gap in available resources, providing high-quality, diverse data to enhance model training, promote generalization, and accelerate automotive design processes. Along with rigorous dataset validation, we also provide ML benchmarking results on the task of aerodynamic drag prediction, showcasing the breadth of applications supported by our dataset. This dataset is set to significantly impact automotive design and broader engineering disciplines by fostering innovation and improving the fidelity of aerodynamic evaluations.
Prospective Ph.D. students - If you are interested in joining the DeCoDE lab, you can apply to the Computational Science and Engineering Program or to the Mechanical Engineering department at MIT.
If you are interested in joining the DeCoDE lab, drop me an email with the following text in your subject line "Join DeCoDE:" followed by the position you are interested in (for example, a postdoc, intern, visiting student, etc.).
Engineering design datasets from the DeCoDE lab. Please cite the corresponding paper when using a dataset.
Interactive project pages for our publications.
Prof. Faez Ahmed is an Associate Professor in the Department of Mechanical Engineering at MIT, where he directs the Design Computation and Digital Engineering Lab. His research interests lie at the intersection of Artificial Intelligence and engineering design, focusing particularly on first‑principle generative AI and optimization algorithms, multi‑modal representation learning, and engineering design methodology with human–AI design co‑pilots. Before joining MIT, Prof. Ahmed was a postdoctoral fellow at Northwestern University and earned his Ph.D. in Mechanical Engineering from the University of Maryland. He also spent several years in Australia’s railway and mining sector, leading data‑driven predictive‑maintenance initiatives. Prof. Ahmed has received the NSF CAREER Award, the ASME DAC Young Investigator Award, the ASME DTM Young Investigator Award, the Google Research Scholar Award, and the Keenan Award for Innovation in Undergraduate Education. At MIT, he has held the Doherty, d’Arbeloff, and ABS Career Development Chairs. He currently serves as an Associate Editor for Computer‑Aided Design and as the Featured Articles Editor for the ASME Journal of Mechanical Design.
Fun fact: According to the Mathematics Genealogy Project, our academic ancestors include: Poisson, Laplace, Lagrange, Euler, Bernoulli, Leibniz, Copernicus, Nasir al-Din al-Tusi, and many more. Check out our academic family tree.
August 2025
CAD-Coder paper by Annie Doris and team received the "DAC Best Paper Award"
August 2025
Dr. Ahmed received the 2025 DAC Young Investigator Award at the ASME IDETC conference.
November 2024
The DeCoDE Lab hosted a workshop with visitors from Toyota Research and Toyota Japan.
August 2024
Dr. Ahmed received the 2024 DTM Young Investigator Award at the ASME IDETC conference.
August 2024
DeCoDE lab members attended and presented work at the ASME IDETC 2024 conference.
August 2024
We conducted the second Data2Design workshop during IDETC 2024. We were grateful for the thoughtful discussions, insightful questions, and genuine engagement from all the attendees. Check out the webpage to see the program here.
May 2024
Dr. Srivastava and Dr. Ahmed co-organized the first-ever InstructLab workshop on LLMs at the MIT IBM office.
May 2024
Dr. Ahmed and Dr. Alam received the Google Research Scholar Award 2024 in Applied Science.
February 2024
Kristen spoke about her research which she and her team published in the ASME Journal of Mechanical Design: ADVISE: Accelerating the Creation of Evidence Syntheses for Global Development Using Natural Language Processing-Supported Human-Artificial Intelligence Collaboration.
December 2023
Rui Zhou won the MIT Pillar AI Collective Fellowship.
December 2023
Lyle, Amin, and DeCoDE alumni Giorgio and Binyang attended and presented their work at NeurIPS 2023.
October 2023
Rui Zhou won the MIT Flagship award and Noah Bagazinski and Kristen Edwards won the runner-up prize at the MIT IGNITE Generative AI Entrepreneurship Competition.
October 2023
Check out the article about Lyle’s work by MIT News.
September 2023
We announced that Dr. Binyang Song had accepted a tenure-track faculty position at Virginia Tech in the Department of Industrial and Systems Engineering. Learn more.
September 2023
We welcomed Annie, Nomi, Nicholas, Rosen, Brandon, and Kaira to the DeCoDE lab.
August 2023
We saw a large number of participants join our workshop during IDETC 2023. We were grateful for the thoughtful discussions, insightful questions, and genuine engagement from all the attendees. Check out the webpage to see the program here.
August 2023
Noah Bagazinski won an award for Exemplary Poster Presentation in Naval Architecture and Ocean Engineering at the NDSEG Fellowship Conference in San Antonio, Texas.
July 2023
Amin received the 2023 Mathworks Fellowship at MIT.
May 2023
Kristen passed the MechE qualifying exams.
April 2023
DeCoDE lab launched its student-run Instagram page follow us.
March 2023
DeCoDE lab celebrated IDETC conference submissions in North End.
February 2023
Kristen and Noah won awards for their posters at MERE 2023.
February 2023
Noah and Amin passed MechE qualifying exams.
January 2023
We thanked Dr. Conrad Tucker and the AiPEX lab team for visiting us and sharing their fantastic work.
August 2022
Amin received the 2022 Mathworks Fellowship at MIT.
August 2022
Noah and Lyle won the third prize at two ASME IDETC Hackathons.
August 2022
Dr. Ahmed won the Honorable Mention award in the JMD editors’ choice award for their PaDGAN paper.
August 2022
Kristen completed her master's degree in MechE.
July 2022
Dr. Ahmed received the 3M Non-Tenured Faculty Award.
June 2022
Noah won the ASME Student Poster Travel Award.
May 2022
Lyle passed MechE qualifying exams and became DeCoDE lab's first Ph.D. candidate.
May 2022
Lyle and Amin completed their master's degrees in MechE.
April 2022
Lyle received honorable mention in the NSF GRFP award.
April 2022
Kristen received the NSF GRFP award.
September 2021
Amin received the 2021 Mathworks Fellowship at MIT.
July 2021
Dr. Ahmed received the 2022 Alumni Excellence Research Award from the UMD Alumni Association.
