LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergenc
Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE). However, PINNs have been shown to perform poorly, sometimes even converging to trivial solutions
https://arxiv.org/abs/2607.14233v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
None
How close the company’s idea is to your thesis statement
Sector
ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market ↓ declining
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Correct facts used on the next screening or memo.
People
N
Nilay Anurag
2.0
low confidence
S
Shital Adhikari
4.4
low confidence
T
Taniya Kapoor
I am an Assistant Professor in the Artificial Intelligence Group at WUR. My research interests lie broadly in Machine Learning for
2.0
low confidence
N
Nikhil Muralidhar
2.0
low confidence