About

Amir Barati Farimani is an Associate Professor of Mechanical Engineering at Carnegie Mellon University, with courtesy appointments in Biomedical Engineering, Chemical Engineering, and Machine Learning. He received his Ph.D. in mechanical science and engineering from the University of Illinois at Urbana-Champaign in 2015, where his thesis focused on detecting and sensing biological molecules using nanopores, utilizing atomistic simulations to understand DNA sensing and detection physics. Following his doctoral studies, he conducted postdoctoral research at Stanford University in Professor Vijay Pande's lab, where he combined machine learning and molecular dynamics to study conformational changes in G-Protein Coupled Receptors, specifically Mu-Opioid Receptors, to elucidate their free energy landscape and activation mechanisms. His research group, the Mechanical and Artificial Intelligence Laboratory (MAIL) at Carnegie Mellon University, is broadly interested in applying machine learning, data science, and molecular dynamics simulations to health and bio-engineering problems. The lab is multidisciplinary, integrating expertise from mechanical, computer science, bio-engineering, physics, material, and chemical engineering fields. The mission of the lab is to bring state-of-the-art machine learning algorithms into mechanical engineering, developing data-driven models that incorporate physics to improve the accuracy of predictive models. His work involves multi-scale simulations such as CFD, MD, and DFT to generate data for these models, with applications spanning additive manufacturing, autonomous vehicles, bioengineering, energy technologies, climate resilience, and human health.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Chemistry
• Mathematics
• Computational chemistry
• Algorithm
• Organic chemistry
• Materials science
• Mathematical analysis
• Mathematical optimization
• Theoretical computer science
• Combinatorial chemistry
• Inorganic chemistry
• Engineering
• Nanotechnology
• Data science
• Computational science
• Structural engineering

Selected publications

• Data from: Recapitulating apicobasal tissue polarity in extracellular matrix-incorporated airway organoids

  DRYAD · 2026-02-18

  datasetOpen access

  The airway epithelium is a dynamic barrier that interfaces with the external environment and internal matrix niche along its apicobasal axis. To recapitulate this tissue arrangement in an organoid format, we present the decellularized ExtraCellular Matrix-incorporated Apical-out Airway Organoid (dECM-AoAO) that integrates native matrix cues, through incorporation of human lung-derived dECM microparticles, without compromising the apical-out polarity. Incorporation of dECM effectively diversifies lineage distribution that better recapitulates native epithelial composition compared to ECM-free AoAO. Harnessing the dECM-AoAO locomotion powered by outward-facing ciliary beating, we developed an experimental and computational pipeline for swarm analysis of organoid group motility as a functional readout of ciliary function. Lastly, dECM-AoAO withstood cryopreservation and reviving with sustained viability, lineage distribution, and ciliary function, enabling future scalability and broad distribution. Together, this work establishes dECM-AoAO as a physiologically relevant and versatile model system for investigating epithelial-ECM interaction during airway homeostasis, disease pathogenesis, and injury responses.

  PublisherDOI

• LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis

  arXiv (Cornell University) · 2026-01-07

  preprintOpen accessSenior author

  Designing mechanical linkages to achieve target end-effector trajectories presents a fundamental challenge due to the intricate coupling between continuous node placements, discrete topological configurations, and nonlinear kinematic constraints. The highly nonlinear motion-to-configuration relationship means small perturbations in joint positions drastically alter trajectories, while the combinatorially expanding design space renders conventional optimization and heuristic methods computationally intractable. We introduce an autoregressive diffusion framework that exploits the dyadic nature of linkage assembly by representing mechanisms as sequentially constructed graphs, where nodes correspond to joints and edges to rigid links. Our approach combines a causal transformer with a Denoising Diffusion Probabilistic Model (DDPM), both conditioned on target trajectories encoded via a transformer encoder. The causal transformer autoregressively predicts discrete topology node-by-node, while the DDPM refines each node's spatial coordinates and edge connectivity to previously generated nodes. This sequential generation enables adaptive trial-and-error synthesis where problematic nodes exhibiting kinematic locking or collisions can be selectively regenerated, allowing autonomous correction of degenerate configurations during design. Our graph-based, data-driven methodology surpasses traditional optimization approaches, enabling scalable inverse design that generalizes to mechanisms with arbitrary node counts. We demonstrate successful synthesis of linkage systems containing up to 20 nodes with extensibility to N-node architectures. This work advances autoregressive graph generation methodologies and computational kinematic synthesis, establishing new paradigms for scalable inverse design of complex mechanical systems.

  PublisherDOI

• Agentic additive manufacturing alloy evaluation

  Additive Manufacturing Letters · 2026-01-10

  articleOpen accessSenior authorCorresponding
  PublisherDOI

• LinkD: AutoRegressive Diffusion Model for Mechanical Linkage Synthesis

  ArXiv.org · 2026-01-07

  articleOpen accessSenior author

  Designing mechanical linkages to achieve target end-effector trajectories presents a fundamental challenge due to the intricate coupling between continuous node placements, discrete topological configurations, and nonlinear kinematic constraints. The highly nonlinear motion-to-configuration relationship means small perturbations in joint positions drastically alter trajectories, while the combinatorially expanding design space renders conventional optimization and heuristic methods computationally intractable. We introduce an autoregressive diffusion framework that exploits the dyadic nature of linkage assembly by representing mechanisms as sequentially constructed graphs, where nodes correspond to joints and edges to rigid links. Our approach combines a causal transformer with a Denoising Diffusion Probabilistic Model (DDPM), both conditioned on target trajectories encoded via a transformer encoder. The causal transformer autoregressively predicts discrete topology node-by-node, while the DDPM refines each node's spatial coordinates and edge connectivity to previously generated nodes. This sequential generation enables adaptive trial-and-error synthesis where problematic nodes exhibiting kinematic locking or collisions can be selectively regenerated, allowing autonomous correction of degenerate configurations during design. Our graph-based, data-driven methodology surpasses traditional optimization approaches, enabling scalable inverse design that generalizes to mechanisms with arbitrary node counts. We demonstrate successful synthesis of linkage systems containing up to 20 nodes with extensibility to N-node architectures. This work advances autoregressive graph generation methodologies and computational kinematic synthesis, establishing new paradigms for scalable inverse design of complex mechanical systems.

  PublisherOA PDF

• PLATO: Planning with LLMs and Affordances for Tool Manipulation

  Journal of Intelligent & Robotic Systems · 2026-04-11 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Abstract As robotic systems become increasingly integrated into complex real-world environments, there is a growing need for approaches that enable robots to understand and act on natural language instructions without extensive pre-programmed knowledge. This paper presents PLATO, a system that addresses this challenge by leveraging specialized large language model agents to process language inputs, understand the environment, predict tool affordances, and generate executable actions. Unlike traditional systems that depend on hard-coded environmental information, PLATO employs a modular agent-based architecture that operates without any initial knowledge of the environment. These agents identify objects and their locations, generate a high-level plan, translate it into low-level actions, and verify successful execution. The system is tested particularly on challenging tool-use tasks, which involve handling diverse objects and require long-horizon planning. PLATO’s design allows it to adapt to dynamic and unstructured settings, enhancing its flexibility and robustness. By evaluating the system across various complex scenarios, we demonstrate its capability to tackle a diverse range of tasks and offer a novel solution to integrate LLMs with robotic platforms, advancing the state-of-the-art in autonomous robotic task execution. For videos and access to our code base, please see our project website: https://sites.google.com/view/plato-anonymous .

  PublisherDOI

• Coupled Diffusion-Encoder Models for Reconstruction of Flow Fields

  ArXiv.org · 2026-01-12

  articleOpen accessSenior author

  Data-driven flow-field reconstruction typically relies on autoencoder architectures that compress high-dimensional states into low-dimensional latent representations. However, classical approaches such as variational autoencoders (VAEs) often struggle to preserve the higher-order statistical structure of fluid flows when subjected to strong compression. We propose DiffCoder, a coupled framework that integrates a probabilistic diffusion model with a conventional convolutional ResNet encoder and trains both components end-to-end. The encoder compresses the flow field into a latent representation, while the diffusion model learns a generative prior over reconstructions conditioned on the compressed state. This design allows DiffCoder to recover distributional and spectral properties that are not strictly required for minimizing pointwise reconstruction loss but are critical for faithfully representing statistical properties of the flow field. We evaluate DiffCoder and VAE baselines across multiple model sizes and compression ratios on a challenging dataset of Kolmogorov flow fields. Under aggressive compression, DiffCoder significantly improves the spectral accuracy while VAEs exhibit substantial degradation. Although both methods show comparable relative L2 reconstruction error, DiffCoder better preserves the underlying distributional structure of the flow. At moderate compression levels, sufficiently large VAEs remain competitive, suggesting that diffusion-based priors provide the greatest benefit when information bottlenecks are severe. These results demonstrate that the generative decoding by diffusion offers a promising path toward compact, statistically consistent representations of complex flow fields.

  PublisherOA PDF

• Agentic additive manufacturing alloy evaluation

  Additive Manufacturing Letters · 2026-01-10 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Agentic systems enable the intelligent use of research tooling, augmenting a researcher’s ability to investigate and propose novel solutions to existing problems. Within Additive Manufacturing (AM), alloy selection and evaluation remains a complex challenge, often requiring expertise in the various domains of materials science, thermodynamic simulations, and experimental analysis. Large Language Model (LLM) enabled agents can facilitate this endeavor by utilizing their extensive knowledge base to dispatch tool calls via Model Context Protocol (MCP) to perform actions such as thermophysical property diagram calculations and lack of fusion process map generation. In addition, the multi-agent system can effectively reason through complex user prompts and provide analysis on the lack of fusion process window of common alloys such as SS316L and IN718 along with proposed composition variants of known alloys. These agents can dynamically adjust their task trajectory to the outcomes of tool call results, effectively enabling autonomous decision-making in practical environments. This work aims to showcase the benefits of adopting a LLM enabled multi-agent system to automate and accelerate the task of evaluating proposed additive manufacturing alloys, both novel and known. • A multi-agent system is developed for the intelligent automation of the proposal, evaluation, and selection of known and novel alloy compositions for the Additive Manufacturing (AM) process. • Material properties of novel alloy compositions are obtained through the generation of thermophysical property diagrams using Thermo-Calc invoked via tool calls. • Print feasibility is assessed by computing lack of fusion process maps using melt pool dimensions generated with Rosenthal’s approximation of a moving heat source. • The Model Context Protocol (MCP) is utilized by the multi-agent system for end-to-end integration with Large Language Model (LLM) providers such as Anthropic, OpenAI, and Google.

  PublisherDOI

• Generative latent neural PDE solver using flow matching

  Machine Learning Science and Technology · 2026-04-24

  articleOpen accessSenior author

  Abstract Autoregressive next-step prediction models have become standard for building data-driven neural solvers to predict time-dependent partial differential equations (PDEs). The use of diffusion models has been shown to enhance the temporal stability of neural solvers, while its stochastic inference mechanism enables ensemble predictions and uncertainty quantification. However, a key drawback of diffusion models is the need to sample a series of discretized timesteps during both training and inference, which increases computational overhead. In addition, most diffusion models operate on structured, uniform grids, limiting their adaptability to irregular domains. To address these shortcomings, we propose a latent flow matching (FM) model for PDE simulation that embeds the PDE state in a lower-dimensional latent space, which reduces computational costs. In addition, we design an autoencoder to map different meshes onto a unified, structured latent grid, which allows predictions on complex geometries. Furthermore, we show that FM can result in faster and more accurate predictions than diffusion-based models, even with a coarser noise schedule. Numerical experiments show that the proposed model outperforms several deterministic and probabilistic baselines in both accuracy and long-term stability, highlighting the potential of FM-based approaches for data-driven PDE learning.

  PublisherDOI

• Coupled Diffusion-Encoder Models for Reconstruction of Flow Fields

  arXiv (Cornell University) · 2026-01-12

  preprintOpen accessSenior author

  Data-driven flow-field reconstruction typically relies on autoencoder architectures that compress high-dimensional states into low-dimensional latent representations. However, classical approaches such as variational autoencoders (VAEs) often struggle to preserve the higher-order statistical structure of fluid flows when subjected to strong compression. We propose DiffCoder, a coupled framework that integrates a probabilistic diffusion model with a conventional convolutional ResNet encoder and trains both components end-to-end. The encoder compresses the flow field into a latent representation, while the diffusion model learns a generative prior over reconstructions conditioned on the compressed state. This design allows DiffCoder to recover distributional and spectral properties that are not strictly required for minimizing pointwise reconstruction loss but are critical for faithfully representing statistical properties of the flow field. We evaluate DiffCoder and VAE baselines across multiple model sizes and compression ratios on a challenging dataset of Kolmogorov flow fields. Under aggressive compression, DiffCoder significantly improves the spectral accuracy while VAEs exhibit substantial degradation. Although both methods show comparable relative L2 reconstruction error, DiffCoder better preserves the underlying distributional structure of the flow. At moderate compression levels, sufficiently large VAEs remain competitive, suggesting that diffusion-based priors provide the greatest benefit when information bottlenecks are severe. These results demonstrate that the generative decoding by diffusion offers a promising path toward compact, statistically consistent representations of complex flow fields.

  PublisherDOI

• LLM-3D print: Large Language Models to monitor and control 3D printing

  Additive manufacturing · 2025-09-01 · 8 citations

  articleOpen accessSenior authorCorresponding

  Industry 4.0 has revolutionized manufacturing by driving digitization and shifting the paradigm toward additive manufacturing (AM). Material extrusion (MEX), a core AM method, produces customized and cost-effective products with minimal waste, challenging traditional subtractive manufacturing. Despite its advantages, MEX remains susceptible to defects that can compromise part quality and function, often requiring expert intervention. Existing rule-based and machine learning approaches struggle to generalize across different printers and sensors, while deep learning methods depend on large labeled datasets, limiting their scalability and adaptability. To address these challenges, we introduce a process monitoring and control framework that employs Large Language Models (LLMs) as autonomous controllers for additive manufacturing. Unlike rule-based or heavily data-dependent approaches, our method requires no domain-specific fine-tuning or training. Instead, the LLM leverages in-context learning, self-prompting, and iterative prompt-reason refinement to evaluate print quality from sequential image captures, detect and classify emerging failure modes, and query and modify the printer for relevant operating parameters. Through this adaptive reasoning process, the LLM not only interprets defects but also improves its own decision-making logic, autonomously formulating and executing corrective actions. This demonstrates a rule-free, self-improving approach to process control that extends beyond traditional quality assurance methods. We validated the effectiveness of the proposed framework by comparing it with a control group of engineers with different levels of AM expertise. The evaluation showed that LLM-based agents not only reliably identified common 3D printing errors such as inconsistent extrusion, stringing, warping, and poor layer adhesion, but also determined their causes and corrected them without human intervention. In addition to matching expert-level accuracy, the LLM was able to recognize emerging print errors earlier than human experts, highlighting its value as a proactive controller. To further demonstrate generalizability, we deployed and tested the framework on two different 3D printers with distinct sensor setups, confirming its adaptability across hardware. We also performed compression tests on baseline prints and on prints optimized by the LLM, with the optimized parts showing clear improvements in mechanical performance. LLMs in continuous improvement cycle LLM-based supervisor agents can be employed at each step of the continuous improvement cycle. The cycle involves evaluating print quality, identifying failure modes, gathering relevant information, and planning and solving the issues by adjusting the print parameters, ensuring high-quality defect-free parts.

  PublisherDOI

Recent grants

• CDS&E: Collaborative Research: Deep learning enhanced parallel computations of fluid flow around moving boundaries on binarized octrees

  NSF · $245k · 2020–2023

Frequent coauthors

• N. R. Aluru

  Walker (United States)

  36 shared

• Francis Ogoke

  34 shared

• Yuyang Wang

  30 shared

• Rishikesh Magar

  Carnegie Mellon University

  30 shared

• Zhonglin Cao

  Guizhou Minzu University

  24 shared

• Kazem Meidani

  20 shared

• Zijie Li

  Institute of High Energy Physics

  18 shared

• Jack Beuth

  17 shared

Education

• Ph.D., Mechanical Science and Engineering

  University of Illinois at Urbana-Champaign

  2015

Awards & honors

• 2023 Engineering Faculty Awards