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I will defend my PhD thesis, “Physics-Constrained Generative Models for Computational Design,” on Tuesday, September 8, 2026, at PM ET. Location: MIT MIT CSAIL 32-G449 Zoom: Committee: Kaiming He, Bill Freeman, Wojciech Matusik Generative models can now propose candidate designs at high throughput, yet the physical world remains the... show more

Minghao Guo

1,369 subscribers

79,092 views • 2 months ago •via X (Twitter)

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36 Comments

Robert Scoble2 months ago

@MIT_CSAIL Your work caught my eye. Thank you and knock it out of the park!

Minghao Guo2 months ago

Relying on downstream validation creates a fundamental trade-off between throughput and physical realizability: generating a candidate may take seconds, while validating it can take hours to months. Physical realizability is a scaling problem, not just a quality problem. (2/7)

Minghao Guo2 months ago

Inspired by Marr’s separation of levels of analysis, we organize generative computational design into three levels: physical, computational, and generative. Because each level abstracts the one below, physical requirements can be weakened or lost at the interfaces between them. (3/7)

Minghao Guo2 months ago

This thesis treats physical requirements as first-class citizens of the generative system. The central idea is to carry those requirements bottom-up through explicit interfaces, from the physical world through computation and into generation. We formulate computational design as constrained optimization. (4/7)

Minghao Guo2 months ago

We realize these interfaces through three core elements: representation, constraints, and objective evaluation. At the representation interface, procedural molecular grammars, medial skeletons, and tetrahedral sphere primitives define candidate spaces that are valid by construction. At the constraint interface, differentiable projection modules enforce physical feasibility during generation. (5/7)

Minghao Guo2 months ago

At the objective interface, unified simulators evaluate design behavior with scientific fidelity at generative throughput, spanning physical regimes from molecular dynamics to aerodynamics. Together, these contributions lay the foundation for universal precision design: general-purpose systems that tailor physically realizable designs to the specific requirements of a problem, individual, or environment, at scale. (6/7)

Minghao Guo2 months ago

A research map of selected publications across my PhD study: #GenerativeAI #AIforScience #ComputationalDesign #ScientificMachineLearning #PhDDefense #worldmodel

Minghao Guo2 months ago

Missing the time haha! It’s 2-3pm ET!

중심2 months ago

@MIT_CSAIL at what time?

BrenJ2 months ago

@MIT_CSAIL @evelovesolive @beffjezos felt like this was up y'alls alley.

DrKnowItAll2 months ago

@MIT_CSAIL Great work! This looks really impressive.

rob2 months ago

@MIT_CSAIL Great stuff here.

samir sawarkar2 months ago

@MIT_CSAIL The next leap in generative AI isn’t generating more designs. It’s generating designs that cannot violate the physics. That’s the shift from “AI can imagine it” to “AI can engineer it.”

Aman Chourasia2 months ago

@MIT_CSAIL This is great!

Daniel Samanez2 months ago

@MIT_CSAIL 👍 at what time is it?

Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social1 month ago

@MIT_CSAIL Congrats. It was a honor to listen to You leading us through Your work. All the best!

Xenonostra'2 months ago

@MIT_CSAIL I wish you the best of luck I'm sure you will do well

GSC2 months ago

@MIT_CSAIL

Zhiyuan2 months ago

@MIT_CSAIL solid work! may you good luck!

Yuxi Xiao2 months ago

@MIT_CSAIL Wow nice video for the phd thesis!!! congrats

Big data annotation2 months ago

@MIT_CSAIL 大佬，牛逼

Raj Kachhadiya (KARAM)2 months ago

@MIT_CSAIL Interesting, congratulations!

Anand Kumar2 months ago

@MIT_CSAIL Interesting. Looking forward to it.

Neil1 month ago

@MIT_CSAIL Wow this is such an interesting research direction, do you think the future of 3D is headed this way?

Yossi Eliaz2 months ago

@MIT_CSAIL loved the video!

likhesh2 months ago

@MIT_CSAIL Amazing, what time exactly?

Mandar Wagh2 months ago

congrats, and that is a committee. the split i keep getting stuck on: physics constraints are mostly differentiable, so they go straight into a loss. manufacturing constraints mostly are not — tool reach, support-free overhang, demoldability, minimum wall are combinatorial, and you cannot gradient your way to a draft angle. does the thesis treat those as one kind of constraint, or as two different problems? genuinely curious which way you landed.

the guy2 months ago

@MIT_CSAIL 🧡

Jesse Jr Lim (林振燊)2 months ago

@MIT_CSAIL wow very cool.. paused the vid alot to see whats going on

X.M.2 months ago

@MIT_CSAIL all the best king

Volatile Markets2 months ago

@MIT_CSAIL Love this!

Verdi2 months ago

@MIT_CSAIL congrats!! you didnt share a time tho? > "on Tuesday, September 8, 2026, at PM ET."

Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)2 months ago

@MIT_CSAIL PhaGGOT

Curtis1 month ago

@MIT_CSAIL Grest work, great slides, great marketing video!

Black Canvas2 months ago

@MIT_CSAIL Wish you the best and interesting work!

Supratik Bhattacharya2 months ago

@MIT_CSAIL Wow! this is super interesting. all the best!

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