José Firmino Aguilar Madeira

Professor with Habilitation in Mathematics at ADM/ISEL/IPL and Integrated Researcher at IDMEC/IST/UL

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Research Highlights
Topology Optimization using Python

ADDRESS

ISEL, Rua Conselheiro Emídio Navarro, 1
1959-007 Lisboa, Portugal
Email: jose.madeira(at)isel.pt

and

IDMEC, IST, Universidade de Lisboa
Ed. de Mecânica 2 - Sala 2.31,
Av. Rovisco Pais, 1049-001 Lisboa, Portugal
Email: aguilarmadeira(at)tecnico.ulisboa.pt

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SCIENTIC INDICATORS

ORCID ID: 0000-0001-9523-3808
Research ID: N-6918-2016
Scopus Author ID: 7003405549
Ciência ID: 6F1E-DCF0-D6EC

EDUCATION

AWARDS AND RECOGNITIONS (since 2004)

Biographical Sketch

Holding expertise in both mathematics and engineering through two Ph.D. programs, he specializes in developing optimization algorithms to tackle real-world challenges. His in-depth understanding of practical issues and mathematical tools allows him to create efficient and robust Nonlinear Optimization algorithms, both with or without derivatives. His innovative input in derivative-free optimization has played a pivotal role in solving practical problems across various domains. He also has an extensive background in optimization research, including numerous publications on derivative-free optimization.

He eagerly presents his submission for the distinguished ERC Advanced Grant 2023, titled "Non-Heuristics Derivative-Free Optimization Methods with Mixed-Variables in MIND." This trailblazing proposal aims to push the boundaries of optimization by merging non-heuristics, derivative-free optimization methods, and the management of mixed-variable problems. The potential impact of this initiative is enormous.

He has three notably published non-heuristic methods in DFO-DSM (Derivative-Free Optimization with Direct Search Methods) intended for continuous variables. First, the DMS (Custódio et al., 2011), a local optimization method capable of handling multiple objectives. Second, the GLODS (Custodio & Madeira, 2015), a Global Optimization (GO) method aimed at optimizing a single objective. Finally, the MultiGLODS (Custódio & Madeira, 2018), another GO method proficient in managing multiple competitive objectives.

Through the application of optimization techniques, his methods have not only proven to be effective but have also pioneered innovative approaches to address optimization challenges across a wide range of engineering, scientific, and industrial sectors. In the field of medicine, he has applied optimization techniques to improve medical processes and interventions, as showcased by the works of Aguilar Madeira et al. (2010) and Ruben et al. (2012). Within material selection, I have played a vital role in enhancing material properties for specific applications, as demonstrated by the research conducted by Leite et al. (2015).

Additionally, he has led breakthroughs in composite materials, evident in the works of Araújo et al. (2013), Franco Correia et al. (2017, 2018), Infante et al. (2019), Luis et al. (2017), Madeira et al. (2017), Madeira, Araujo et al. (2015), Madeira, Araújo et al. (2015), and Monte et al. (2017). His expertise extends to various domains, including micromechanical modeling (Jayachandran et al., 2018), constrained layer damping (Madeira et al., 2020), meshless methods (Roque et al., 2014, 2015; Roque & Madeira, 2018), specimen optimization (Baptista et al., 2014, 2015, 2016, 2017, 2022), cold-formed steel (Madeira, Dias et al., 2015), fracture analysis (Baptista et al., 2016), railway systems (Magalhães et al., 2016, 2017), energy optimization (Segurado et al., 2016), impact analysis (P. J. P. Moita et al., 2018), injury prevention (P. P. Moita et al., 2019; Pires Moita et al., 2018), civil engineering applications (Garrido et al., 2019; Kiss et al., 2020), crashworthiness investigations (Castro et al., 2019, 2020; Fontana et al., 2020; Santos et al., 2017), noise reduction strategies (Araújo et al., 2019; Araújo & Aguilar Madeira, 2020; Araújo & Madeira, 2020; Cotrim et al., 2023; Madeira & Araujo, 2020; Pires et al., 2014), functionally graded material layers (Correia et al., 2019; Moleiro et al., 2020, 2021), feature selection techniques (Türkşen et al., 2013), aerospace engineering (Pires et al., 2014), e damage assessment (Araújo dos Santos et al., 2019).

Most Cited/Selected Papers (in June 2023)