67 skills found · Page 1 of 3
maziarraissi / PINNsPhysics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
ehsanhaghighat / SciannDeep learning for Engineers - Physics Informed Deep Learning
AmeyaJagtap / XPINNsExtended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
alexpapados / Physics Informed Deep Learning Solid And Fluid MechanicsUsing Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
Omar-Karimov / ChartScanAIChartScanAI is an advanced app for detecting patterns in stock and cryptocurrency charts using deep learning and YOLOv8. It automates chart pattern recognition, providing traders with a powerful tool for making informed decisions. Key features include real-time analysis, high accuracy for Buy/Sell signals, and support for various charts.
PredictiveIntelligenceLab / USNCCM15 Short Course Recent Advances In Physics Informed Deep LearningNo description available
tensordiffeq / TensorDiffEqEfficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Jianxun-Wang / PIMBRLPhysics-informed Dyna-style model-based deep reinforcement learning for dynamic control
SydneyBioX / BIDCellBiologically-informed deep learning for cell segmentation of subcelluar spatial transcriptomics data
Otutu11 / A Multi Stage Hybrid Deep Learning Framework For Interpretable Anomaly Detection A hybrid deep learning framework combining multiple models for accurate, interpretable anomaly detection in environmental sensor networks, enhancing data reliability, identifying faults or unusual patterns, and supporting informed environmental monitoring and decision-making.
oscar-rincon / ReScience PINNsReplication with PyTorch of ''Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations'' by M. Raissi, P. Perdikaris, and G.E. Karniadakis from 2019.
Veleslavia / VimssVisually-informed Music Source Separation project at Jeju 2018 Deep Learning Summer Camp
BBahtiri / Deep Learning Constitutive ModelA physics-informed deep learning (DL)-based constitutive model for investigating epoxy based composites under different ambient conditions.
Rui1521 / Turbulent Flow NetsTowards Physics-informed Deep Learning for Turbulent Flow Prediction
kostyanoob / PowerPhysics informed, deep-learning-based state estimation for distribution electrical grids. The study proposes using physical properties of the grid connectivity as a regularizer of a deep neural network training.
Aghoreshwar / Awesome Customer AnalyticsCustomer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza. SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services. In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics. By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant. With the digital age things have changed drastically. Customer issuperman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience. This tsunami of data has changed the customer analytics forever. Today customer analytics is not only restricted to marketing forchurn and retention but more focus is going on how to improve thecustomer experience and is done by every department of the organization. A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics. From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation. Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure. Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before. Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical. There are various ways customer analytics is carried out: Acquiring all the customer data Understanding the customer journey Applying big data concepts to customer relationships Finding high propensity prospects Upselling by identifying related products and interests Generating customer loyalty by discovering response patterns Predicting customer lifetime value (CLV) Identifying dissatisfied customers & churn patterns Applying predictive analytics Implementing continuous improvement Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time. Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect. Tomorrow there may not be just plain simple customer sentiment analytics based on feedback or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time. There’s no doubt that customer analytics is absolutely essential for brand survival.
deepmorzaria / Physics Informed Neural Network PINNs TF 2.0Tensoflow 2 implementation of physics informed deep learning.
AI4PFAS / AI4PFASDataset and code for "Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity"
oliverchampion / IVIMNETThis repository contains the code regarding our publication: Improved unsupervised physics-informed deep learning for intravoxel-incoherent motion modeling and evaluation in pancreatic cancer patients
BiswajitPadhi99 / Predicting Cloud CPU Utilization On Azure Dataset Using DeeplearningMany companies are utilizing the cloud for their day to day activities. Many big cloud service providers like AWS, Microsoft Azure have been success-fully serving its increasing customer base. A brief understanding of the char-acteristics of production virtual machine (VM) workloads of large cloud pro-viders can inform the providers resource management systems, e.g. VM scheduler, power manager, server health manager. In our project we will be analysing Microsoft Azure’s VM CPU utilization dataset released in October 2017. We predict the VM workload from the CPU usage pattern like mini-mum, maximum and average from the Azure dataset. Different techniques among Deep learning are used for the prediction by considering the history of the workload. By considering real VM traces, we can show that the predic-tion-informed schedules increase utilization and stop physical resource ex-haustion. We can arrive at a conclusion that cloud service providers can use their workloads’ characteristics and machine learning techniques to enhance resource management greatly.
