ProFlow: Zero-Shot Physics-Consistent Sampling via Proximal Flow Guidance | ScienceToStartup | ScienceToStartup

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References (47)

[1]

Terminally constrained flow-based generative models from an optimal control perspective

2026Wei Gao, Ming Li et al.

[2]

Tree Reward-Aligned Search for TReASURe in Masked Diffusion Language Models

2025Zichao Yu, Ming Li et al.

[3]

PIRF: Physics-Informed Reward Fine-Tuning for Diffusion Models

Founder's Pitch

"ProFlow enables efficient zero-shot physics-consistent sampling from sparse data with pre-trained generative models, without retraining."

Generative Models for Physics•Score: 7•View PDF ↗

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ProFlow: Zero-Shot Physics-Consistent Sampling via Proximal Flow Guidance

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References (47)

Founder's Pitch

"ProFlow enables efficient zero-shot physics-consistent sampling from sparse data with pre-trained generative models, without retraining."

Commercial Viability Breakdown

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ProFlow: Zero-Shot Physics-Consistent Sampling via Proximal Flow Guidance

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References (47)

Founder's Pitch

"ProFlow enables efficient zero-shot physics-consistent sampling from sparse data with pre-trained generative models, without retraining."

Commercial Viability Breakdown

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