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Physicist Richard Feynman's forgotten notes on 'the restaurant problem' finally deciphered after 50 years
Physicist Richard Feynman's forgotten notes on 'the restaurant problem' finally deciphered after 50 years Researchers cracked a 50-year-old math problem scribbled by Richard Feynman over lunch. The equations show that humans are better decision-makers than scientists once thought. It started with a plate of ginger chicken.
How a Richard Feynman formula could explain your dining habits in a new city
June 2, 2026 report How a Richard Feynman formula could explain your dining habits in a new city Paul Arnold Author Gaby Clark Scientific Editor Robert Egan Associate Editor One of the dilemmas facing anyone in a new and unfamiliar city is where to dine out. You might consult guides, speak to locals, check reviews, and ultimately, try your luck. But if you're there for a while, at some point you're going to be asking yourself whether to visit new eateries or stick to the ones you've already...
Scientists uncover Feynman’s formula for finding best holiday restaurant
Late physicist turned issue of when to stop searching for a better place to eat into mathematical problemWhen it comes to exploring a new city, it can be tricky to know when to stop searching for a different restaurant to try every night, or to visit the first place you love on repeat. Now researchers have found that the late physicist and Nobel laureate Richard Feynman devised a mathematical equation that can tackle the conundrum – at least when the range of options is known – and they...
Scientists uncover Feynman’s formula for finding best holiday restaurant
Late physicist turned issue of when to stop searching for a better place to eat into mathematical problemWhen it comes to exploring a new city, it can be tricky to know when to stop searching for a different restaurant to try every night, or to visit the first place you love on repeat. Now researchers have found that the late physicist and Nobel laureate Richard Feynman devised a mathematical equation that can tackle the conundrum – at least when the range of options is known – and they...
Taming the Loss Landscape of PINNs with Noisy Feynman-Kac Supervision: Operator Preconditioning and Non-Asymptotic Error Bounds
arXiv:2606.00643v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often train slowly or fail to converge on challenging partial differential equations (PDEs), a behavior recently linked to severely ill-conditioned loss landscapes inherited from the underlying differential operator. We study PINNs augmented with a pointwise data-fidelity term, added at a few points in the domain to the standard residual and boundary losses. We show that this supervision term acts as...
Feynmann's solved ‘restaurant dilemma’ 50 years ago — now a study confirms his mathematics
Nature, Published online: 01 June 2026; doi:10.1038/d41586-026-00821-4An experiment with 2,520 participants backs Richard Feynman’s answer to every diner’s dilemma: do I want to try something new?
Your phone can use tiny skin-colour changes to measure your heart rate
Nature, Published online: 03 June 2026; doi:10.1038/d41586-026-01793-1Passive heart-rate monitoring during regular phone use could provide early warning of health issues — plus, testing Richard Feynman’s solution to the ‘restaurant dilemma’ problem.
Synthics: Synthetic Physics-like Datasets for Machine Learning
Announce Type: new Abstract: Representative data is fundamental in machine learning, as limited data hinders generalisation. Collecting sufficient real-world samples is often infeasible. Synthetic data generation offers a practical solution, but only if the generated data faithfully reflects the structure of real observations.
Guided Discovery of New Behaviors using Diffusion Policies
arXiv:2606.08743v1 Announce Type: new Abstract: Diffusion models have become a powerful tool for generative modeling in robotics, with diffusion policies excelling at modeling multimodal action-trajectory distributions. However, when demonstrations are limited, standard sampling often reproduces dominant behaviors while neglecting valid but rare modes, limiting the discovery of novel solutions. Existing approaches, such as guidance methods or combining reinforcement learning with diffusion,...
Decomposable Neuro Symbolic Regression
Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. However, most SR methods prioritize minimizing prediction error over identifying the governing equations, often producing overly complex or inaccurate expressions. To address this, we present a decomposable SR method that generates interpretable multivariate expressions leveraging transformer models, genetic...