
AMLD Workshop Generative AI for Forward and Inverse Design in Architecture and Engineering

Presentation
Traditionally, architectural and engineering design involves combining and optimizing many criteria and constraints. For performance-driven design, architects and engineers create parametric design models to generate, simulate, and evaluate many design instances, to gather performance feedback on design alterations. However, this is typically a challenging process, especially in the case of large design problems, limiting the designers to only investigate a narrow spectrum of possible solutions.
At the SDSC, in collaboration with Gramazio Kohler and the group on Concrete Structures and Bridge Design at ETH Zürich, we have developed the tool "AI-eXtended Design" for generative design for parametric modeling. During this workshop, you will be able to explore its possibilities and understand how to harness it to enhance your design workflows using machine learning.
Prerequisites
- Attendees should bring their laptop, and preferably pre-install the AIXD tools and Python environments required.
- The workshop is best suited for practitioners and scientists with at least intermediate experience in Python. It is aimed at architects and engineers with basic coding skills who want to leverage machine learning in their work, as well as computer/data scientists interested in applying their skills in the architecture or engineering domain.
* Recommended: basic understanding of machine learning, generative AI, experience with Python, Jupyter notebook, Pandas.
Details
Co-organised with
📅 Date: Sunday, 24 March
🕒 Time: 14:00 - 17:30
🎫 Get tickets here: https://go.epfl.ch/AMLD
Programme
Presenters
- Aleksandra Anna Apolinarska, Gramazio Kohler, ETHZ, Switzerland
- Michael Kraus, Concrete Structures and Bridge Design, ETHZ, Switzerland
- Luis Salamanca, SDSC, ETHZ and EPFL, Switzerland
14:00 - 15:30 Presentation: Introduction to generative AI for forward and inverse Design
15:30 - 16:00 Coffee break
16:00 - 17:30 Workshop: Applications and hands-on coding with AIXD toolbox
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Roberto holds an M.Sc. and a Ph.D. in Particle Physics from the University of Torino, Italy. He has worked for several years in fundamental research as a senior fellow and data scientist at the CERN Experimental Physics division and on a research project supported by the Belgian National Fund for Scientific Research (FNRS). In 2018 he moved to EPFL to work on data mining and Machine Learning techniques for the built environment and renewable energies. He has started and led multiple collaborations with academic and industry partners in the energy domain. Roberto joined the SDSC in September 2021 as a Principal Data Scientist with the mission of accompanying industries, NGOs and international organizations through their data science journey.


Rok obtained a B.A. in Physics from Washington University in St.Louis in 2003. After obtaining his PhD in theoretical Astrophysics from the University of Washington in 2010, Rok spent several years as a Postdoctoral researcher at the Institute for Computational Science, University of Zürich. Seeking new challenges, he moved to the ETH Scientific IT Services group, where he helped researchers across different ETH domains solve their (big) data analysis problems. He specialized in optimizing and scaling up data analysis tasks by mapping them to high-performance computing resources. Since July 2017 he has been at the Swiss Data Science Center developing Renku, the Center's data science platform.

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Silvia holds an MSc in Computer Science from EPFL and a PhD in Computer Science from the University of York, UK. She has been a senior research fellow at the University of Trento and later at Politecnico di Milano, Italy. Here, she had the chance to work on Marie Curie and ERC projects relating to natural language processing. From 2012 to 2019, she was a Senior Manager and NLP expert at ELCA Informatique Switzerland, whose AI department she helped create and expand. Silvia joined the Swiss Data Science Center in 2019 and is currently its Chief Transformation Officer, in charge of the team leading organizations to digital transformation.


Saurabh Bhargava, joined the SDSC as a Principal Data Scientist in the Industry Cell at the Zürich office in 2022. Saurabh previously worked in the retail sector and the advertising industry in Germany. He lead and built various data products for customers using state of the art machine learning methods and industrializing them thereby adding value for the customers. He completed his PhD from ETH Zürich in June 2017 specializing in machine learning applications on Audio data. He obtained his Master’s and Bachelor’s degrees from EPFL and Indian Institute of Technology (IIT), Roorkee, India in 2011 and 2009 respectively. His interests and expertise are in combining state of the art data science and data engineering tools for building scalable data products.
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