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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

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79,092 görüntüleme • 2 ay önce •via X (Twitter)

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Robert Scoble profil fotoğrafı

Robert Scoble2 ay önce

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

Minghao Guo profil fotoğrafı

Minghao Guo2 ay önce

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 Guo profil fotoğrafı

Minghao Guo2 ay önce

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 Guo profil fotoğrafı

Minghao Guo2 ay önce

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 Guo profil fotoğrafı

Minghao Guo2 ay önce

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 Guo profil fotoğrafı

Minghao Guo2 ay önce

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 Guo profil fotoğrafı

Minghao Guo2 ay önce

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

Minghao Guo profil fotoğrafı

Minghao Guo2 ay önce

Missing the time haha! It’s 2-3pm ET!

중심 profil fotoğrafı

중심2 ay önce

@MIT_CSAIL at what time?

BrenJ profil fotoğrafı

BrenJ2 ay önce

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

DrKnowItAll profil fotoğrafı

DrKnowItAll2 ay önce

@MIT_CSAIL Great work! This looks really impressive.

rob profil fotoğrafı

rob2 ay önce

@MIT_CSAIL Great stuff here.

samir sawarkar profil fotoğrafı

samir sawarkar2 ay önce

@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 Chourasia profil fotoğrafı

Aman Chourasia2 ay önce

@MIT_CSAIL This is great!

Daniel Samanez profil fotoğrafı

Daniel Samanez2 ay önce

@MIT_CSAIL 👍 at what time is it?

Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social profil fotoğrafı

Ralf Sigmund🇺🇦🇪🇺 @sistar-hh.bsky.social1 ay önce

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

Xenonostra' profil fotoğrafı

Xenonostra'2 ay önce

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

GSC profil fotoğrafı

GSC2 ay önce

@MIT_CSAIL

Zhiyuan profil fotoğrafı

Zhiyuan2 ay önce

@MIT_CSAIL solid work! may you good luck!

Yuxi Xiao profil fotoğrafı

Yuxi Xiao2 ay önce

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

Big data annotation profil fotoğrafı

Big data annotation2 ay önce

@MIT_CSAIL 大佬，牛逼

Raj Kachhadiya (KARAM) profil fotoğrafı

Raj Kachhadiya (KARAM)2 ay önce

@MIT_CSAIL Interesting, congratulations!

Anand Kumar profil fotoğrafı

Anand Kumar2 ay önce

@MIT_CSAIL Interesting. Looking forward to it.

Neil profil fotoğrafı

Neil1 ay önce

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

Yossi Eliaz profil fotoğrafı

Yossi Eliaz2 ay önce

@MIT_CSAIL loved the video!

likhesh profil fotoğrafı

likhesh2 ay önce

@MIT_CSAIL Amazing, what time exactly?

Mandar Wagh profil fotoğrafı

Mandar Wagh2 ay önce

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 guy profil fotoğrafı

the guy2 ay önce

@MIT_CSAIL 🧡

Jesse Jr Lim (林振燊) profil fotoğrafı

Jesse Jr Lim (林振燊)2 ay önce

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

X.M. profil fotoğrafı

X.M.2 ay önce

@MIT_CSAIL all the best king

Volatile Markets profil fotoğrafı

Volatile Markets2 ay önce

@MIT_CSAIL Love this!

Verdi profil fotoğrafı

Verdi2 ay önce

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

Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ) profil fotoğrafı

Jacques Mₜ=∏ₛ((1−λₛ)+λₛEₛ)2 ay önce

@MIT_CSAIL PhaGGOT

Curtis profil fotoğrafı

Curtis1 ay önce

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

Black Canvas profil fotoğrafı

Black Canvas2 ay önce

@MIT_CSAIL Wish you the best and interesting work!

Supratik Bhattacharya profil fotoğrafı

Supratik Bhattacharya2 ay önce

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

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Humanoids daily

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Researchers at MIT developed Draft, a tool that lets engineers design and customize robots by adjusting parameters such as limb length, body size, and motor power. It uses real-world hardware data and reinforcement learning to estimate robot feasibility and test performance in simulation. The tool helps researchers explore robot designs faster without rebuilding CAD models for every change.

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