The DeepRadioID system was evaluated with a testbed of 20 bit-similar SDRs, as well as on two datasets containing transmissions from 500 ADS-B devices and by 500 WiFi devices. Human walking under physical loads and deep learning algorithm. Interleaved: the full physical simulation is interleaved and combined with an output from a deep neural network; this requires a fully differentiable simulator and represents the tightest coupling between the physical system and the learning process. to deal with problems such as adaptive beam management and rate selection. This is no exception for deep learning-based algorithms. The core feature that distinguishes learning-based devices is that digital signal processing (DSP) decisions are driven by deep neural networks (DNNs). ∙ Originally developed to support DARPA’s spectrum collaboration challenge in 2019, Colosseum can emulate up to 256x256 4-tap wireless channels among 128 software-defined radios. solve, and how tightly the physical model is integrated into the Another major challenge of mmWave and THz communications is the severe path and absorption loss (e.g., oxygen at 60 GHz). Real-Time Fine Tuning. present the capability of ADCME for learning spatially-varying physical parameters using deep neural networks [16, 17, 18]. Robust Physical-World Attacks on Deep Learning Visual Classification Kevin Eykholt∗1, Ivan Evtimov*2, Earlence Fernandes2, Bo Li3, Amir Rahmati4, Chaowei Xiao1, Atul Prakash1, Tadayoshi Kohno2, and Dawn Song3 1University of Michigan, Ann Arbor 2University of Washington 3University of California, Berkeley 4Samsung Research America and Stony Brook University 04/21/2020 ∙ by Francesco Restuccia, et al. PDF: https://arxiv.org/pdf/2008.09768, Learning Compositional Koopman Operators for Model-Based Control , PDF: https://arxiv.org/pdf/2009.14339, Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution , A neural network can be defined as a standard machine learning function f m, which given an input x returns a prediction y and prediction-confidence conf; i.e. PDF: https://arxiv.org/pdf/2009.14280, Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization , Techniques able to perform real-time fine-grained spectrum optimization will thus become fundamental to squeeze out any spectrum resource available to wireless devices. paper, we first discuss the need for real-time deep learning at the physical Moreover, the received waveforms still need to be decodable and thus cannot be extensively modified. … Stephen Chew has written thoughtfully about this point. It has been shown that deep learning algorithms can outperform traditional feature-based algorithms in identifying large populations of devices [shawabka2020exposing]. PDF: https://arxiv.org/pdf/1904.03538, Data-driven discretization: a method for systematic coarse graining of partial differential equations , The codes in this repository are based on the eponymous research project A Deep Learning Framework for Assessing Physical Rehabilitation Exercises.The proposed framework for automated quality assessment of physical rehabilitation exercises encompasses metrics for quantifying movement performance, scoring … To make an example, Figure 4(a) shows the approach based on two-dimensional (2D) convolution proposed in [Restuccia-infocom2019]. PDF: https://arxiv.org/pdf/2005.06549.pdf, Transformers for Modeling Physical Systems , Abstract. The framework of physics-guided neural networks (PGNN) aims to integrate knowledge of physics in deep learning methods, to produce physically consistent outputs of neural networks. The Physical (Bodily-Kinesthetic) Learning Style If the physical style is more like you, it's likely that you use your body and sense of touch to learn about the world around you. PDF: https://ge.in.tum.de/publications/2019-multi-pass-gan/, A Study of Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations , Deep learning is a general approach to artificial intelligence that involves AI that acts as an input to other AI. The first work to propose a systematic investigation into the above issues is [Restuccia-infocom2019]. PDF: https://arxiv.org/pdf/1903.00033, Deep neural networks for data-driven LES closure models , The target of this section is to discuss existing system-level challenges in physical-layer deep learning as well as the state of the art in addressing these issues. Please let us know if we've overlooked Project+Code: https://ge.in.tum.de/publications/2020-iclr-prantl/, ScalarFlow: A Large-Scale Volumetric Data Set of Real-world Scalar Transport Flows for Computer Animation and Machine Learning , Nowadays, deep learning models usually have millions of parameters (e.g., AlexNet has some 60M weights) or perhaps also tens of millions, e.g., VGG-16, with about 138M. It is very well understood what deep neural networks (DNNs) actually learn as discriminating features in computer vision applications. share. Project+Code: https://ge.in.tum.de/publications/tempogan/, Deep Fluids: A Generative Network for Parameterized Fluid Simulations , of deep learning and numerical simulations. PDF: https://arxiv.org/pdf/1908.10515, Computing interface curvature from volume fractions: A machine learning approach , Particularly, we also show the transitions corresponding to the points (1) to (3) in the upper side of Figure 4(a). Due to the above reasons, the wireless community has recently started to acknowledge that radically-novel propositions are needed to achieve both real-time and effective wireless spectrum optimization. The rush of interest in deep learning from the wireless community is not without a reason. download the GitHub extension for Visual Studio, https://ge.in.tum.de/publications/2020-um-solver-in-the-loop/, https://github.com/pangeo-data/WeatherBench, https://ge.in.tum.de/publications/2020-lsim-kohl/, https://ge.in.tum.de/publications/2020-iclr-holl/, https://openreview.net/forum?id=B1lDoJSYDH, https://ge.in.tum.de/publications/2020-iclr-prantl/, https://ge.in.tum.de/publications/2019-tog-eckert/, https://ge.in.tum.de/publications/tempogan/, http://www.byungsoo.me/project/deep-fluids/, https://ge.in.tum.de/publications/latent-space-physics/, https://ge.in.tum.de/publications/2019-multi-pass-gan/, https://github.com/thunil/Deep-Flow-Prediction, http://ge.in.tum.de/publications/2017-sig-chu/, https://ge.in.tum.de/publications/2018-mlflip-um/, https://ge.in.tum.de/publications/2017-prantl-defonn/, https://proceedings.icml.cc/static/paper_files/icml/2020/6414-Paper.pdf, https://www.sciencedirect.com/science/article/pii/S0021999119306151, https://www.sciencedirect.com/science/article/abs/pii/S0045793019302282, http://papers.nips.cc/paper/8138-deep-dynamical-modeling-and-control-of-unsteady-fluid-flows, https://cims.nyu.edu/~schlacht/CNNFluids.htm, https://www.labxing.com/files/lab_publications/2259-1524535041-QiPuSd6O.pdf, https://github.com/zhong1wan/data-assisted, https://proceedings.icml.cc/static/paper_files/icml/2020/1323-Paper.pdf, https://proceedings.icml.cc/static/paper_files/icml/2020/15-Paper.pdf, https://github.com/USC-Melady/ICLR2020-PADGN, http://papers.nips.cc/paper/9672-hamiltonian-neural-networks.pdf, http://proceedings.mlr.press/v97/greenfeld19a/greenfeld19a.pdf, http://www.dgp.toronto.edu/projects/latent-space-dynamics/, http://www.gmrv.es/Publications/2019/SOC19/, https://link.springer.com/article/10.1007/s40304-017-0103-z, https://github.com/yuanming-hu/difftaichi. We propose a novel deep learning model for spatio-temporal modeling of skeletal data, for application in rehabilitation assessment. PDF: https://arxiv.org/pdf/1912.00873, Poisson CNN: Convolutional Neural Networks for the Solution of the Poisson Equation with Varying Meshes and Dirichlet Boundary Conditions , When realized concretely, spectrum-driven optimization will realize the dream of a cognitive radio first envisioned more than 20 years ago by Mitola and Maguire [mitola1999cognitive]. Interleaved approaches are especially important for More recently, researchers have demonstrated the existence of universal perturbation vectors, such that when applied to the majority of inputs, the classifier steers to a class different than the original one. Although yielding optimal solutions, these approaches are usually NP-Hard, and thus, unable to be run in real time and address spectrum-level issues. Moreover, they rely on a series of modeling assumptions (e.g., fading/noise distribution, traffic and mobility patterns, and so on) that may not always be valid in highly-dynamic IoT contexts. As deep learning was not conceived having the constraints and requirements of wireless communications in mind, it is still unclear what are the fundamental limitations of physical-layer deep learning and how far we can leverage its power to address ever more complex problems. There are three books that I think you must own physical copies of if you are a neural network practitioner. layer, and then summarize the current state of the art and existing We hope that this paper will inspire and spur significant wireless research efforts in this timely and exciting field in the years to come. Project+Code: https://ge.in.tum.de/publications/2019-tog-eckert/, tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow , In this paper, the authors propose RFLearn, a hardware/software framework to integrate a Python-level CNN into the DSP chain of a radio receiver. Track Proc. Alongside PAWR, the Colosseum network emulator [Colosseum] will be soon open to the research community and provide us with unprecedented data collection opportunities. 0 Apart from forward or inverse, the type of integration between learning 0 Critically, this allows not only to save hardware resources, but also to keep both latency and energy consumption constant, which are highly-desirable features in embedded systems design and are particular critical in wireless systems, as explained in Section. Project: http://gamma.cs.unc.edu/DRL_FluidRigid/, DeepMimic, Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills , We point out that although channel statistics could be stationary in some cases, and therefore could theoretically be learned, (i) these statistics cannot be valid in every possible network situation; (ii) a CNN cannot be trained on all possible channel distributions and related realizations; (iii) a CNN is hardly re-trainable in real-time due to its sheer size. The role of deep learning in science is at a turning point, with weather, climate, and Earth systems modeling emerging as an exciting application area for physics-informed deep learning that can more effectively identify nonlinear relationships in large datasets, extract patterns, emulate complex physical processes, and build predictive models. PDF: https://arxiv.org/pdf/1807.10300, Fluid directed rigid body control using deep reinforcement learning , PDF: https://arxiv.org/pdf/2011.04217.pdf, Fourier Neural Operator for Parametric Partial Differential Equations , Specifically, we first introduce the notion of physical-layer deep learning in Section II, and discuss the related requirements and challenges in III, as well as the existing state of the art. The first one is the unavoidable noise and fading that is inherent to any wireless transmission. PDF: https://arxiv.org/pdf/1905.11169, Unsupervised Intuitive Physics from Past Experiences , PDF: http://proceedings.mlr.press/v97/greenfeld19a/greenfeld19a.pdf, Latent-space Dynamics for Reduced Deformable Simulation , PDF: https://arxiv.org/pdf/1708.00588, Data-assisted reduced-order modeling of extreme events in complex dynamical systems , PDF: https://arxiv.org/pdf/1712.07854, Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks , It primarily collects links to the work of the I15 lab at TUM, as PDF: https://arxiv.org/pdf/1812.10972, Neural Material: Learning Elastic Constitutive Material and Damping Models from Sparse Data , The first kind of attack is called targeted, where given a valid input, a classifier and a target class, it is possible to find an input close to the valid one such that the classifier is “steered” toward the target class.