Maryam Shakiba News

Seminar: Towards Computational Modeling of Materials Under Space Environmental Conditions - Sept. 19

Jeff Zehnder — Thu, 11 Sep 2025 16:54:30 +0000

Seminar: Towards Computational Modeling of Materials Under Space Environmental Conditions - Sept. 19 Jeff Zehnder Thu, 09/11/2025 - 10:54

Maryam Shakiba
Assistant Professor, Smead Aerospace
Friday, Sept. 19 | 10:40 A.M. | AERO 111

Abstract: Elastomers and polymers such as silicone and Kapton have a wide range of applications across engineering disciplines, including structural components and thermal shields in space structures. In these applications, the materials are often exposed to high temperatures and ultraviolet (UV) radiation, which compromises their mechanical performance and overall functionality. To understand and predict the degradation of these materials, we formulate constitutive equations that explicitly link changes in the macromolecular network to the resulting mechanical response. Our models utilize chemical and physical testing to characterize the macromolecular network and thereby predict stress–strain behavior and brittle failure. We integrate phase-field methods to capture fracture and validate our models against independent experimental data. The overarching objective is to provide predictive capabilities for material performance under coupled extreme space environments.

An even more challenging problem from the modeling perspective arises when these polymers are used in manufacturing fiber-reinforced composites. For such systems, we establish efficient numerical frameworks with robust constitutive equations to study stress distributions and crack progression in two-dimensional laminate representations. We also develop deep learning frameworks capable of predicting both elastic and post-failure full-field stress distributions and crack patterns in composites directly from their microstructures.

Bio: Maryam Shakiba is an assistant professor at the Aerospace Engineering Sciences Department at the ��ƷSM��ӰƬ. Before joining CU, she was and assistant professor at Virginia Tech and a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign. She received her Ph.D. from Texas A&M University and her B.S. and M.S. degrees from Tehran Polytechnic. Shakiba’s group develops physics, chemistry, and mechanics-based constitutive equations to simulate multi-physics conditions for different advanced materials. The group also devises high-fidelity as well as mechanistic machine-learning approaches to solve engineering problems. Our goals are to (1) develop theoretical frameworks to understand advanced material responses under extreme multi-factor conditions and (2) integrate the theoretical framework with machine learning approaches, as physics-based machine learning is the key technology to creating true digital twins. Shakiba is the recipient of the AFOSR Young Investigator Program (YIP) award to investigate additively manufactured composites for high-temperature applications and the NSF CAREER award to understand the multi-physics mechanisms that cause macroplastics fragmentation and generate microplastics.

Elastomers and polymers such as silicone and Kapton have a wide range of applications across engineering disciplines, including structural components and thermal shields in space structures. In these applications, the...

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$750,000 grant to advance naval aviation materials research

Jeff Zehnder — Tue, 02 Sep 2025 15:30:41 +0000

$750,000 grant to advance naval aviation materials research Jeff Zehnder Tue, 09/02/2025 - 09:30

Jeff Zehnder

Maryam Shakiba is studying complex composite materials with machine learning to make stronger and lighter aircraft for the Navy.

Shakiba, an assistant professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences, is leading a $750,000 grant from the Office of Naval Research, Aerospace Structures and Materials, to use machine learning techniques to advance composites made with additive manufacturing – more commonly known as 3D printing.

“Additive manufacturing has advanced a lot in the last few years,” Shakiba said. “We can now print complex, fiber-reinforced composite materials. Because we can print more complex patterns, we also need fast computational approaches that can model and predict the response of those materials.”

Navy aircraft technology has generally used metal body panels, but are starting to rely more on composite materials, like passenger jets have for years. Modeling the performance of such materials prior to construction is critical to determining their strength and potential failure points.

Traditionally, this requires finite elements analysis, a tried-and-true method of mathematical modeling. However, the complexity of the method demands major computing resources.

“If you have a material and you change one parameter, a finite elements simulation takes a few days. We need faster models to explore the design space better,” she said.

Shakiba’s work in machine learning is opening new opportunities for that modeling.

“We’ve integrated a convolutional neural network and a graph neural network that increases accuracy and decreases the amount of data you need to put in to get good results. The preliminary results show you can reduce the training data by at least 50 percent,” Shakiba said.

Even with a need for dramatically less data, the work requires supercomputers, like ��ƷSM��ӰƬ’s Blanca cluster, but the results are spit out in seconds instead of days.

Over the course of the three-year grant, Shakiba and her team, which includes partners at Johns Hopkins University, will advance these machine learning tools with increasingly complex composite patterns. The goal is to combine analysis of materials at both micro- and macro-scale to develop a complete picture of a composite’s response to stress.

“There is a huge interest from the federal government in decreasing the amount of time it takes to design to using a material it in the field,” Shakiba said. “Our method can do that.”

Maryam Shakiba is studying complex composite materials with machine learning to make stronger and lighter aircraft for the Navy. Shakiba is leading a $750,000 grant from the Office of Naval Research, using machine learning techniques to...

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Seminar - Physics-based Modeling of Materials: Finite Element and Data-Driven Approaches - Sept. 15

Anonymous — Thu, 07 Sep 2023 06:00:00 +0000

Seminar - Physics-based Modeling of Materials: Finite Element and Data-Driven Approaches - Sept. 15 Anonymous (not verified) Thu, 09/07/2023 - 00:00

Maryam Shakiba
Assistant Professor, Smead Aerospace
Friday, Sept. 15 | 10:40 a.m. | AERO 120

Abstract: This presentation discusses computational modeling of complex materials behaviors under multi-physics conditions for Aerospace applications. We develop chemistry, physics, and mechanics-based constitutive equations to explain complex systems. Then, we devise high-fidelity numerical ap-proaches and mechanistic machine learning to solve our problems.

The first part of the presentation focuses on progressive damage in fiber-reinforced composites. In such composites, cracks initiate around the fibers aligned transversely to the loading direction. The transverse cracks can cause leakage in specific applications or progress to inter-ply delamination and catastrophic failure. We integrate an efficient numerical framework with robust and accurate constitutive equations to study transverse behavior and multiple cracking of two-dimensional representations of fiber-reinforced composite laminates. We then develop deep learning frameworks to predict the elastic and post-failure full-field stress distribution and the crack pattern in two-dimensional representations of the composites based on their microstructures.

The second part of the presentation focuses on developing chemistry, physics, and mechanics-based constitutive equations to predict the stress and brittle failure responses of polymeric materials under multi-physics degradation. We connect the changes in the macromolecular network of materials due to multi-physics conditioning to their mechanical responses. The changes in the macromolecular network are obtained based on chemical characterization tests. The obtained constitutive equations predict the stress-strain response until failure with phase-field to capture the induced brittle failure. The constitutive equations are verified versus independent mechanical tests available in the literature for different types of materials.

Bio: Maryam Shakiba is an assistant professor at the Aerospace Engineering Sciences Department at the ��ƷSM��ӰƬ. Before joining ��ƷSM��ӰƬ, she was and assistant professor at Virginia Tech and a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign. She received her Ph.D. from Texas A&M University and her B.S. and M.S. degrees from Tehran Polytechnic. Shakiba's group develops physics, chemistry, and mechanics-based constitutive equations to simulate multi-physics conditions for different advanced materials. The group also devises high-fidelity as well as mechanistic machine-learning approaches to solve engineering problems. Our goals are to (1) de-velop theoretical frameworks to understand advanced material responses under extreme multi-factor conditions and (2) integrate the theoretical framework with machine learning approaches, as physics-based machine learning is the key technology to creating true digital twins. Shakiba is the recipient of the AFOSR Young Investigator Program (YIP) award to investigate additively manufactured compos-ites for high-temperature applications and the NSF CAREER award to understand the multi-physics mechanisms that cause macroplastics fragmentation and generate microplastics.

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Meet Assistant Professor Maryam Shakiba

Anonymous — Mon, 15 Aug 2022 15:17:58 +0000

Meet Assistant Professor Maryam Shakiba Anonymous (not verified) Mon, 08/15/2022 - 09:17

Maryam Shakiba is joining the Ann and H.J. Smead Department of Aerospace Engineering Sciences in August 2022 as an assistant professor.

Before joining ��ƷSM��ӰƬ, she was an assistant professor at Virginia Tech. While at Virginia Tech, she was recognized with an AFOSR Young Investigator Program (YIP) award, and three NSF awards, including the NSF CAREER award.

Her research focuses on computational modeling of high-performance materials under coupled effects of mechanical loading and extreme conditions.

Her research goal is twofold. First, developing theoretical frameworks to understand advanced material responses under extreme multi-physics conditions. Second, integrating the theoretical framework with machine learning approaches as physics-based machine learning is key to creating true digital twins.

This combination will enable the design of high-performance, smart and multi-functional materials. Moreover, such improved physics, chemistry, mechanic, and data-based models allow the address of challenges such as manufacturing innovative designs for extreme conditions and creating digital twins for efficient autonomy.

She has been working in collaboration with Mechanical Engineers, Materials Scientists, and Chemists to design high-temperature resistant and additively manufactured composites, create fatigue-resistant superelastic composites for infrastructure applications, retrofit structures under a highly corrosive environment, and tackle plastic degradation and pollution in the ocean and atmosphere.

Shakiba earned a Bachelor and Master’s of Science in Civil Engineering from The University of Amirkabir and earned her Doctor of Philosophy in Structural Engineering from Texas A&M University in December 2013. Then, she was a post-doctoral research associate at the University of Illinois at Urbana-Champaign, working on a project in collaboration with the Air Force Research Lab (AFRL) and funded by the AFOSR.

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