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<h2>Renato G. Nascimento</h2>
<h3>AI Applied Scientist</h3>
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<h1>Biography</h1>
<p>I’m an Applied Scientist and AI Engineer with over 15 years of R&D experience in software for consumer and industrial applications. I’ve developed and deployed AI solutions, empowering data science teams to build and visualize multiple analytics models and techniques at scale, deployed to diverse cloud services. With a BSc in Computer Science and Ph.D. in Mechanical Engineering, I’ve created novel methods combining physics-based knowledge with Deep Learning frameworks creating hybrid AI solutions. In my research in collaboration with the Diagnostics and Prognostics lab at NASA Ames, we enabled fast and accurate monitoring of Lithium-ion batteries with probabilistic hybrid machine learning.</p>
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<li>Physics-Informed Neural Networks</li>
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<p class="course">PhD in Mechanical Engineering, 2022</p>
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<h1>Featured Publications</h1>
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<span>Renato G. Nascimento</span>, <span>Felipe A. C. Viana</span>, <span>Matteo Corbetta</span>, <span>Chetan S. Kulkarni</span>
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<span class="article-date">
September 2021
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<em>Journal of Power Sources</em>
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<h3 class="article-title mb-1 mt-3">
<a href="/publication/2021-jps-nascimento-et-al/">Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis</a>
</h3>
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<p>Lithium-ion batteries are commonly used to power unmanned aircraft vehicles (UAVs). The ability to model and forecast remaining useful life of these batteries enables UAV reliability assurance. Building principled accurate models is challenging due to the complex electrochemistry that governs battery operation. Alternatively, reduced order models have the advantage of capturing the overall behavior of battery discharge, although they suffer from simplifications and residual discrepancy. This paper presents a hybrid modeling approach that directly implements physics within deep neural networks. While most of the input–output relationship is captured by reduced-order models, data-driven kernels reduce the gap between predictions and observations. A reduced-order model based on Nernst and Butler–Volmer equations represents the overall battery discharge, and a multilayer perceptron models the battery non-ideal voltage. Battery aging is characterized by time-dependent internal resistance and the amount of available Li-ions, which are modeled through an ensemble of variational Bayesian multilayer perceptrons. The approach is validated using data publicly available through the NASA Prognostics Center of Excellence website. Results showed that our hybrid battery prognosis model can be successfully calibrated, even with a limited number of observations. Moreover, the model can help optimizing battery operation by offering long-term forecast of battery capacity.</p>
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<h4 class="card-title exp-title text-muted mt-0 mb-1">AI Applied Scientist</h4>
<h4 class="card-title exp-company text-muted my-0">Intel</h4>
<div class="text-muted exp-meta">
May 2022 –
Present
<span class="middot-divider"></span>
<span>Santa Clara, CA</span>
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<div class="card-text">AI Emerging Tecnologies</div>
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<h4 class="card-title exp-title text-muted mt-0 mb-1">Solutions Architect I Inter</h4>
<h4 class="card-title exp-company text-muted my-0">Amazon</h4>
<div class="text-muted exp-meta">
Jun 2020 –
Aug 2020
<span class="middot-divider"></span>
<span>Cupertino, CA</span>
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<div class="card-text">Developed deep learning solutions for the Neuron SDK and AWS Inferentia</div>
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<h4 class="card-title exp-title text-muted mt-0 mb-1">Intern with KBR</h4>
<h4 class="card-title exp-company text-muted my-0">NASA Ames Research Center</h4>
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May 2020 –
Aug 2020
<span class="middot-divider"></span>
<span>Moffett Field, CA</span>
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<div class="card-text">Diagnostics and prognostics modeling of drone powertrain systems with physics-informed machine learning</div>
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<h4 class="card-title exp-title text-muted mt-0 mb-1">Graduate Research Assistant</h4>
<h4 class="card-title exp-company text-muted my-0"><a href="https://pml-ucf.github.io/" target="_blank" rel="noopener">Probabilistic Mechanics Laboratory @ UCF</a></h4>
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Aug 2018 –
May 2022
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<span>Orlando, FL</span>
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<div class="card-text">Key Projects:</p>
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<li>Physics-informed neural networks for cumulative damage modeling applied in fatigue estimation of aircraft fuselage panels.</li>
<li>Reverse engineering quadrotor drone dynamics with physics-informed neural networks.
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