1. INI-VPINN: A Variational Physics-Informed Neural Network with Implicit Neumann and Interface Handling for Multi-Material Domains with Geometric Singularities

   http://arxiv.org/abs/2606.18032v1 · arxiv

   We propose a new weak-form Physics-Informed Neural Network approach (named INI-VPINN). INI-VPINN naturally incorporates Neumann boundary and interface conditions into the variational formulation. It removes the need for additional loss terms or multiple subdomain networks. This framework employs com

2. Spectrum to all orders of Polchinski-Strominger {Effective} String Theory of Polyakov-Liouville Type

   http://arxiv.org/abs/0910.5615v2 · arxiv

   The spectrum of a Polchinski-Strominger type effective string theory, extended to all orders, herein called an effective string theory of the \emph{Polyakov-Liouville Type} (for obvious reasons) is investigated to all orders in the small parameter $R^{-1}$. Here $R$ is the length of the \emph{closed

3. A No-go theorem for de Sitter compactifications?

   http://arxiv.org/abs/hep-th/0205056v1 · arxiv

   A general framework for studying compactifications in supergravity and string theories was introduced by Candelas, Horowitz, Strominger and Witten. This was further generalised to take into account the warp factor by de Wit, Smit and Hari Dass. Though the prime focus of the latter was to find soluti

4. A Model of Interface Growth with non-Burgers Dynamical Exponent

   http://arxiv.org/abs/cond-mat/9704139v1 · arxiv

   We define a new model of interface roughening which has the property that the minimum of interface height is conserved locally during the growth. This model corresponds to the limit $q \to \infty$ of the q-color dimer deposition-evaporation model introduced by us earlier [Hari Menon M K and Dhar D 1

5. A laser-microfabricated electrohydrodynamic thruster for centimeter-scale aerial robots

   http://arxiv.org/abs/1906.10210v4 · arxiv

   To date, insect scale robots capable of controlled flight have used flapping wings for generating lift, but this requires a complex and failure-prone mechanism. A simpler alternative is electrohydrodynamic (EHD) thrust, which requires no moving mechanical parts. In EHD, corona discharge generates a

6. Effective String Theories (EST) of Yang-Mills Flux Tubes

   http://arxiv.org/abs/2312.10629v2 · arxiv

   This chapter explains the concept of \emph{Effective String Theories}(EST), and their success in explaining the results that Yang-Mills flux tubes behave, to a high degree of accuracy, like Bosonic Strings(BST). It describes EST's of Lüscher and Weisz, and their principal conclusions. It then discus

7. Spontaneous Interlayer Charge Transfer near the Magnetic Quantum Limit

   http://arxiv.org/abs/cond-mat/9709288v1 · arxiv

   Experiments reveal that a confined electron system with two equally-populated layers at zero magnetic field can spontaneously break this symmetry through an interlayer charge transfer near the magnetic quantum limit. New fractional quantum Hall states at unusual total filling factors such as ν= 11/1

8. Gradually Truncated Power law distribution - Citation of scientists

   http://arxiv.org/abs/cond-mat/0112049v1 · arxiv

   Gradually Truncated Power law distribution - Citation of scientists Hari M. Gupta, Jose R. Campanha and Bianca A. Ferrari Unesp - Physics Dpto. - Rio Claro Sao Paulo - Brazil Abstract The number of times, a scientist is cited in other scientific publications is now an important factor in his m

9. Deep Interpretable Models of Theory of Mind

   http://arxiv.org/abs/2104.02938v2 · arxiv

   When developing AI systems that interact with humans, it is essential to design both a system that can understand humans, and a system that humans can understand. Most deep network based agent-modeling approaches are 1) not interpretable and 2) only model external behavior, ignoring internal mental

10. SlimNets: An Exploration of Deep Model Compression and Acceleration

    http://arxiv.org/abs/1808.00496v1 · arxiv

    Deep neural networks have achieved increasingly accurate results on a wide variety of complex tasks. However, much of this improvement is due to the growing use and availability of computational resources (e.g use of GPUs, more layers, more parameters, etc). Most state-of-the-art deep networks, desp

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